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Chapter 3: Emission Inventory

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CHAPTER 3 EMISSION INVENTORY

3.1 Introduction................................................................................................................ 3-2

3.2 Modeling Domain and Geographic Regions............................................................ 3-3

3.3 Development of 2002 Baseline Inventory................................................................. 3-5

3.3.1 Outline of Methodology..................................................................................... 3-5

3.3.2 Near Port Emissions .......................................................................................... 3-6

3.3.3 Interport Emissions ......................................................................................... 3-79

3.3.4 2002 Baseline Emission Inventories ............................................................... 3-97

3.4 Development of 2020 and 2030 Scenarios.............................................................. 3-99

3.4.1 Outline of Methodology................................................................................... 3-99

3.4.2 Growth Factors by Geographic Region ......................................................... 3-99

3.4.3 Emission Controls in Baseline and Control Scenarios ............................... 3-118

3.4.4 Calculation of Near Port and Interport Inventories................................... 3-124

3.4.5 2020 and 2030 Baseline Inventories.............................................................. 3-131

3.4.6 2020 and 2030 Control Inventories .............................................................. 3-133

3.5 Estimated Category 3 Inventory Contribution ................................................... 3-135

3.5.1 Baseline Contribution of C3 Vessels to National Level Inventory ............ 3-135

3.5.2 Contribution to Mobile Source Inventories for Selected Cities................. 3-139

3.6 Projected Emission Reductions ............................................................................ 3-140

3.7 Inventories Used for Air Quality Modeling......................................................... 3-141

Regulatory Impact Analysis

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CHAPTER 3 Emission Inventory

3.1 Introduction

Ships (i.e., ocean-going vessels) are significant contributors to the total United States (U.S.) mobile source emission inventory. The U.S. ship inventory reported here focuses on Category 3 (C3) vessels, which use C3 engines for propulsion. C3 engines are defined as having displacement above 30 liters per cylinder (L/cyl). The resulting inventory includes emissions from both propulsion and auxiliary engines used on these vessels, as well as those on gas and steam turbine vessels.

Most of the vessels operating in U.S. ports that have propulsion engines less than 30 liters per cylinder are domestic and are already subject to strict national standards affecting NOX, PM, and fuel sulfur content. As such, the inventory does not include any ships, foreign or domestic, powered by Category 1 or Category 2 (i.e., <30 L/cyl) engines. In addition, as discussed in Sections 3.3.2.5 and 3.3.3.2, this inventory is primarily based on activity data for ships that carry foreign cargo. Category 3 vessels carrying domestic cargo that operate only between U.S. ports are only partially accounted for in this inventory.1 Emissions due to military vessels are also excluded.

The regional and national inventories for C3 vessels presented in this chapter are sums of independently constructed port and interport emissions inventories. Port inventories were developed for 89 deep water and 28 Great Lake ports in the U.S.2 While there are more than 117 ports in the U.S., these are the top U.S. ports in terms of cargo tonnage. Port-specific emissions were calculated with a “bottom-up” approach, using data for vessel calls, emission factors, and activity for each port. Interport emissions were obtained using the Waterway Network Ship Traffic, Energy and Environment Model (STEEM).3,4 STEEM also uses a “bottom-up” approach, estimating emissions from C3 vessels using historical North American shipping activity, ship characteristics, and activity-based emission factors. STEEM was used to quantify and geographically (i.e., spatially) represent interport vessel traffic and emissions for vessels traveling within 200 nautical miles (nm) of the U.S.

The detailed port inventories were spatially merged into the STEEM gridded inventory to create a comprehensive inventory for Category 3 vessels. For the 117 ports, this involved removing the near-port portion of the STEEM inventory and replacing it with the detailed port inventories. For the remaining U.S. ports for which detailed port inventories are not available, the near-port portion of the STEEM inventory was simply retained. This was done for a base year of 2002. Inventories for 2020 were then projected using regional growth rates5,6 and adjustment factors to account for the International Maritime Organization (IMO) Tier 1 and Tier 2 NOX standards and NOX retrofit program.2 Inventories incorporating additional Tier 3 NOX and fuel sulfur controls within the proposed Emission Control Area (ECA) were also developed for 2020 and 2030.

This chapter details the methodologies used to create the baseline and future year inventories and presents the resulting inventories for the U.S. Section 3.2 describes the modeling domain and geographic regions used in this analysis. Section 3.3 describes the methodology and


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results for the 2002 base year inventory. Section 3.4 follows with a discussion of the growth rates and methodology used to create the 2020 and 2030 baseline and control inventories. Section 3.5 presents the estimated contribution of Category 3 vessels to U.S. national and local inventories. Section 3.6 follows with estimates of the projected emission reductions due to the proposed control program. Section 3.7 concludes the chapter by describing the changes in the inventories between the baseline scenarios used for the air quality modeling and the updated baseline scenarios in this proposed rule.

The inventory estimates reported in this chapter include emissions out to 200 nm from the U.S. coastline, including Alaska and Hawaii, but not extending into the Exclusive Economic Zone (EEZ) of neighboring countries. Inventories are presented for the following pollutants: oxides of nitrogen (NOX), particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), hydrocarbons (HC), carbon monoxide (CO), and carbon dioxide (CO2). The PM inventories include directly emitted PM only, although secondary sulfates are taken into account in the air quality modeling.

3.2 Modeling Domain and Geographic Regions

The inventories described in this chapter reflect ship operations that occur within the area that extends 200 nautical miles (nm) from the official U.S. baseline, which is recognized as the low-water line along the coast as marked on the official U.S. nautical charts in accordance with the articles of the Law of the Sea. This boundary is roughly equivalent to the border of the U.S Exclusive Economic Zone. The U.S. region was then clipped to the boundaries of the U.S. Exclusive Economic Zone. The boundary was divided into regions using geographic information system (GIS) shapefiles obtained from the National Oceanic and Atmospheric Administration, Office of Coast Survey.7 The accuracy of the NOAA shapefiles was verified with images obtained from the U.S. Geological Survey. The confirmed NOAA shapefiles were then combined with a shapefile of the U.S. international border from the National Atlas.8

The resulting region was further subdivided for this analysis to create regions that were compatible with the geographic scope of the regional growth rates, which are used to project emission inventories for the years 2020 and 2030, as described later in this document.

• The Pacific Coast region was split into separate North Pacific and South Pacific regions along a horizontal line originating from the Washington/Oregon border (Latitude 46° 15’ North).

• The East Coast and Gulf of Mexico regions were divided along a vertical line roughly

drawn through Key Largo (Longitude 80° 26’ West).

• The Alaska region was divided into separate Alaska Southeast and Alaska West regions along a straight line intersecting the cities of Naknek and Kodiak. The Alaska Southeast region includes most of the State’s population, and the Alaska West region includes the emissions from ships on a great circle route along the Aleutian Islands between Asia and the U.S. West Coast.


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• For the Great Lakes domain, a similar approach was used to create shapefiles containing all the ports and inland waterways in the near port inventory and extending out into the lakes to the international border with Canada. The modeling domain spanned from Lake Superior on the west to the point eastward in the State of New York where the St. Lawrence River parts from U.S. soil.

• The Hawaiian domain was subdivided so that a distance of 200 nm beyond the

southeastern islands of Hawaii, Maui, Oahu, Molokai, Niihau, Kauai, Lanai, and Kahoolawe was contained in Hawaii East. The remainder of the Hawaiian Region was then designated Hawaii West.

This methodology resulted in nine separate regional modeling domains that are identified below and shown in Figure 3-1. U.S. territories are not included in this analysis.

• South Pacific (SP) • North Pacific (NP) • East Coast (EC) • Gulf Coast (GC) • Alaska Southeast (AE) • Alaska West (AW) • Hawaii East (HE) • Hawaii West (HW) • Great Lakes (GL)

AEAW

HWHE

SP

NPGL

GCEC

Figure 3-1 Regional Modeling Domains


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3.3 Development of 2002 Baseline Inventory

This section describes the methodology and inputs, and presents the resulting inventories for the 2002 baseline calendar year. The first section describes the general methodology. The second section describes the methodology, inputs, and results for near port emissions. The third section describes the methodology and inputs for emissions when operating away from port (also referred to as “interport” emissions). The fourth section describes the method for merging the interport and near port portions of the inventory. Resulting total emissions for the U.S., as well as for nine geographic regions within the U.S., are then presented.

3.3.1 Outline of Methodology

The total inventory was created by summing emissions estimates for ships while at port (near port inventories) and while underway (interport inventories). Near port inventories for calendar year 2002 were developed for 117 U.S. commercial ports that engage in foreign trade. Based on an ICF International analysis,9 these 117 commercial ports encompass nearly all U.S. C3 vessel calls.10

The outer boundaries of the ports are defined as 25 nm from the terminus of the reduced speed zone for deep water ports and 7 nm from the terminus of the reduced speed zone for Great Lake ports. Port emissions are calculated for different modes of operation and then summed. Emissions for each mode are calculated using port-specific information for vessel calls, vessel characteristics, and activity, as well as other inputs that vary instead by vessel or engine type (e.g., emission factors).

The interport inventory was estimated using the Waterway Network Ship Traffic, Energy, and Environmental Model (STEEM).3,4 The model geographically characterizes emissions from ships traveling along shipping lanes to and from individual ports, in addition to the emissions from vessels transiting near the ports. The shipping lanes were identified from actual ship positioning reports. The model then uses detailed information about ship destinations, ship attributes (e.g., vessel speed and engine horsepower), and emission factors to produce spatially allocated (i.e., gridded) emission estimates for ships engaged in foreign commerce.

The 117 near port inventories are an improvement upon STEEM’s near port results in several ways. First, the precision associated with STEEM’s use of ship positioning data may be less accurate in some locations, especially as the lanes approach shorelines where ships would need to follow more prescribed paths. Second, the STEEM model includes a maneuvering operational mode (i.e., reduced speed) that is generally assumed to occur for the first and last 20 kilometers of each trip when a ship is leaving or entering a port. In reality, the distance when a ship is traveling at reduced speeds varies by port. Also, the distance a ship traverses at reduced speeds often consists of two operational modes: a reduced speed zone (RSZ) as a ship enters or leaves the port area and actual maneuvering at a very low speed near the dock. Third, the STEEM model assumes that the maneuvering distance occurs at an engine load of 20 percent, which represents a vessel speed of approximately 60 percent of cruise speed. This is considerably faster than ships would maneuver near the docks. The single maneuvering speed assumed by STEEM also does not reflect the fact that the reduced speed zone, and therefore emissions, may vary by port. Fourth, and finally, the STEEM model does not include the


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emissions from auxiliary engines during hotelling operations at the port. The near-port inventories correct these issues.

The regional emission inventories produced by the current STEEM interport model are most accurate for vessels while cruising in ocean or Great Lakes shipping lanes, and the near port inventories, which use more detailed local port information, are significantly more accurate near the ports. Therefore, the inventories in this analysis are derived by merging together: (1) the near port inventories, which extend 25 nautical miles and 7 nautical miles from the terminus of the RSZ for deep water ports and Great Lake ports, respectively, and (2) the remaining interport portion of the STEEM inventory, which extends from the endpoint of the near port inventories to the 200 nautical mile boundary or international border with Canada, as appropriate. Near some ports, a portion of the underlying STEEM emissions were retained if it was determined that the STEEM emissions included ships traversing the area near a port, but not actually entering or exiting the port.

3.3.2 Near Port Emissions

Near port inventories for calendar year 2002 were developed for ocean-going vessels at 89 deep water and 28 Great Lake ports in the U.S. The inventories include emissions from both propulsion and auxiliary engines on these vessels.

This section first describes the selection of the ports for analysis and then provides the methodology used to develop the near port inventories. This is followed by a description of the key inputs. Total emissions by port and pollutant for 2002 are then presented. The work summarized here was conducted by ICF International under contract to EPA.2 The ICF documentation provides more detailed information.2

3.3.2.1 Selection of Individual Ports to be Analyzed

All 150 deep sea and Great Lake ports in the Principal Ports of the United States dataset11 were used as a starting point. Thirty ports which had no foreign traffic were eliminated because there is no information in the U.S. Army Corps of Engineers (USACE) entrances and clearances data about domestic traffic. (See Section 3.3.2.5 for a further discussion of domestic traffic and how it is accounted for in this study). In addition, two U.S. Territory ports in Puerto Rico were removed as these were outside the area of interest for this study. Several California ports were added to the principle ports list because ARB provided the necessary data and estimates for those ports. This is discussed in Section 3.3.2.4.1. Also, a conglomerate port in the Puget Sound area was added as discussed in Section 3.3.2.4.2. The final list of 117 deep sea and Great Lake ports, along with their coordinates, is given in the Appendix, Table 3-102.

3.3.2.2 Port Methodology

Near port emissions for each port are calculated for four modes of operation: (1) hotelling, (2) maneuvering, (3) reduced speed zone (RSZ), and (4) cruise. Hotelling, or dwelling, occurs while the vessel is docked or anchored near a dock, and only the auxiliary engine(s) are being used to provide power to meet the ship’s energy needs. Maneuvering occurs within a very short distance of the docks. The RSZ varies from port to port, though generally the


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RSZ would begin and end when the pilots board or disembark, and typically occurs when the near port shipping lanes reach unconstrained ocean shipping lanes. The cruise mode emissions in the near ports analysis extend 25 nautical miles beyond the end of the RSZ lanes for deep water ports and 7 nautical miles for Great Lake ports.

Emissions are calculated separately for propulsion and auxiliary engines. The basic equation used is as follows:

Equation 3-1 )/10()()()()/()()( 6

][][modmod][][mod gtonnesAdjEFLFcallhrsPcallsEmissions engengeeengenge−××××××=

Where: Emissionsmode [eng] = Metric tonnes emitted by mode and engine type Calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[eng] = Total engine power by engine type, in kilowatts hrs/callmode = Hours per call by mode LFmode [eng] = Load factor by mode and engine type (unitless) EF[eng] = Emission factor by engine type for the pollutant of interest, in g/kW-hr

(these vary as a function of engine type and fuel used, rather than activity mode) Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 10-6 = Conversion factor from grams to metric tonnes

Main engine load factors are calculated directly from the propeller curve based upon the

cube of actual speed divided by maximum speed (at 100% maximum continuous rating [MCR]). In addition, cruise mode activity is based on cruise distance and speed inputs. The following sections provide the specific equations used to calculate propulsion and auxiliary emissions for each activity mode.

3.3.2.2.1 Cruise

Cruise emissions are calculated for both propulsion (main) and auxiliary engines. The basic equation used to calculate cruise mode emissions for the main engines is:

Equation 3-2 )/10()()()/()()( 6

][][][][ gtonnesEFLFcallhrsPcallsEmissions mainmaincruisecruisemainmaincruise−×××××=

Where: Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode Calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[main] = Total main engine power, in kilowatts hrs/callcruise = Hours per call for cruise mode LFcruise [main] = Load factor for main engines in cruise mode (unitless) EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these

vary as a function of engine type and fuel used, rather than activity mode) 10-6 = Conversion factor from grams to metric tonnes

In addition, the time in cruise is calculated as follows:


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Equation 3-3

calltripsknotsSpeedCruisenmilesceDisCruisecallHrs cruise /2][/][tan/ ×=

Where: Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles

for Great Lake ports) Cruise speed = vessel service speed, in knots 2 trips/call = Used to calculate round trip cruise distance

Main engine load factors are calculated directly from the propeller curve based upon the cube of actual speed divided by maximum speed (at 100% maximum continuous rating [MCR]):

Equation 3-4 ( )3][ ][/][ knotsSpeedMaximumknotsSpeedCruiseLoadFactor maincruise =

Since cruise speed is estimated at 94 percent of maximum speed12, the load factor for

main engines at cruise is 0.83.

Substituting Equation 3-3 for time in cruise into Equation 3-2, and using the load factor of 0.83, the equation used to calculate cruise mode emissions for the main engines becomes the following:

Equation 3-5 Cruise Mode Emissions for Main Engines )/10()(83.0)/2()/tan()()( 6

][][][ gtonnesEFcalltripsSpeedCruiseceDisCruisePcallsEmissions mainmainmaincruise−××××××=

Where: Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[main] = Total main engine power, in kilowatts Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles

for Great Lake ports) Cruise speed = vessel service speed, in knots 2 trips/call = Used to calculate round trip cruise distance 0.83 = Load factor for main engines in cruise mode, unitless EF [main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these


The equation used to calculate cruise mode emissions for the auxiliary engines is:

Equation 3-6 Cruise Mode Emissions for Auxiliary Engines )/10()()()/2()/tan()()( 6

][][][][ gtonnesEFLFcalltripsSpeedCruiseceDisCruisePcallsEmissions auxauxcruiseauxauxcruise−××××××=

Where: Emissionscruise[aux] = Metric tonnes emitted from auxiliary engines in cruise mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[aux] = Total auxiliary engine power, in kilowatts


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Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles for Great Lake ports)

Cruise speed = vessel service speed, in knots 2 trips/call = Used to calculate round trip cruise distance LFcruise [aux] = Load factor for auxiliary engines in cruise mode, unitless (these vary by ship

type and activity mode) EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these


The inputs of calls, cruise distance, and vessel speed are the same for main and auxiliary

engines. Relative to the main engines, auxiliary engines have separate inputs for engine power, load factor, and emission factors. The activity-related inputs, such as engine power, vessel speed, and calls, can be unique to each ship calling on a port, if ship-specific information is available. For this analysis, as discussed in Section 3.3.2.3.1.1, these inputs were developed by port for bins that varied by ship type, engine type, and dead weight tonnage (DWT) range.

3.3.2.2.2 Reduced Speed Zone

RSZ emissions are calculated for both propulsion (main) and auxiliary engines. The basic equation used to calculate RSZ mode emissions for the main engines is:

Equation 3-7 )/10()()()()/()()( 6

][][][][ gtonnesAdjEFLFcallhrsPcallsEmissions mainmainRSZRSZmainmainRSZ−××××××=

Where: EmissionsRSZ[main] = Metric tonnes emitted from main engines in RSZ mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[main] = Total main engine power, in kilowatts hrs/callRSZ = Hours per call for RSZ mode LFRSZ [main] = Load factor for main engines in RSZ mode, unitless EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these

vary as a function of engine type and fuel used, rather than activity mode) Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 10-6 = Conversion factor from grams to metric tonnes

In addition, the time in RSZ mode is calculated as follows:

Equation 3-8 calltripsknotsSpeedRSZnmilesceDisRSZcallHrs RSZ /2][/][tan/ ×=

Load factor during the RSZ mode is calculated as follows:

Equation 3-9 ( )3][ / SpeedMaximumSpeedRSZLoadFactor mainRSZ =

In addition:


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Equation 3-10 94.0/SpeedCruiseSpeedMaximum =

Where: 0.94 = Fraction of cruise speed to maximum speed

Substituting

Equation 3-10 into Equation 3-9, the equation to calculate load factor becomes:

Equation 3-11 ( )3][ /94.0 SpeedCruiseSpeedRSZLoadFactor mainRSZ ×=


Load factors below 2 percent were set to 2 percent as a minimum.

Substituting Equation 3-8 for time in mode and Equation 3-11 for load factor into Equation 3-7 , the expression used to calculate RSZ mode emissions for the main engines becomes:

Equation 3-12 RSZ Mode Emissions for Main Engines

( ) ( ) )/10()(/94.0)/2()/tan()()( 6][

3][][ gtonnesAdjEFSpeedCruiseSpeedRSZcalltripsSpeedRSZceDisRSZPcallsEmissions auxauxauxRSZ

−××××××××= Where: EmissionsRSZ[main] = Metric tonnes emitted from main engines in RSZ mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[main] = Total main engine power, in kilowatts RSZ distance = one way distance, in nautical miles (specific to each port) RSZ speed = speed, in knots (specific to each port) 2 trips/call = Used to calculate round trip RSZ distance Cruise speed = vessel service speed, in knots EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these

vary as a function of engine type and fuel used, rather than activity mode) Adj = Low load adjustment factor, unitless (used when the load factor is below 0.20) 10-6 = Conversion factor from grams to tons 0.94 = Fraction of cruise speed to maximum speed

Emission factors are considered to be relatively constant down to about 20 percent load. Below that threshold, emission factors tend to increase significantly as the load decreases. During the RSZ mode, load factors can fall below 20 percent. Low load multiplicative adjustment factors were developed and applied when the load falls below 20 percent (0.20). If the load factor is 0.20 or greater, the low load adjustment factor is set to 1.0.

The equation used to calculate RSZ mode emissions for the auxiliary engines is:

Equation 3-13 RSZ Mode Emissions for Auxiliary Engines )/10()()()/2()/tan()()( 6

][][][][ gtonnesEFLFcalltripsSpeedRSZceDisRSZPcallsEmissions auxauxRSZauxauxRSZ−××××××=


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Where: EmissionsRSZ[aux] = Metric tonnes emitted from auxiliary engines in RSZ mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[aux] = Total auxiliary engine power, in kilowatts RSZ distance = one way distance, in nautical miles (specific to each port) RSZ speed = speed, in knots (specific to each port) 2 trips/call = Used to calculate round trip cruise distance LFRSZ [aux] = Load factor for auxiliary engines in RSZ mode, unitless (these vary by ship type

and activity mode) EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these


Unlike main engines, there is no need for a low load adjustment factor for auxiliary engines, because of the way they are generally operated. When only low loads are needed, one or more engines are shut off, allowing the remaining engines to maintain operation at a more efficient level.

The inputs of calls, RSZ distance, and RSZ speed are the same for main and auxiliary engines. Relative to the main engines, auxiliary engines have separate inputs for engine power, load factor, and emission factors. The RSZ distances vary by port rather than vessel or engine type. Some RSZ speeds vary by ship type, while others vary by DWT. Mostly, however, RSZ speed is constant for all ships entering the harbor area. All Great Lake ports have reduced speed zone distances of three nautical miles occurring at halfway between cruise speed and maneuvering speed.

3.3.2.2.3 Maneuvering

Maneuvering emissions are calculated for both propulsion (main) and auxiliary engines. The basic equation used to calculate maneuvering mode emissions for the main engines is:

Equation 3-14 )/10()()()()/()()( 6

][][][][ gtonnesAdjEFLFcallhrsPcallsEmissions mainmainmanmanmainmainman−××××××=

Where: Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[main] = Total main engine power, in kilowatts hrs/callman = Hours per call for maneuvering mode LFman [main] = Load factor for main engines in maneuvering mode, unitless EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these


Maneuvering time-in-mode is estimated based on the distance a ship travels from the breakwater or port entrance to the pier/wharf/dock (PWD). Maneuvering times also include shifts from one PWD to another or from one port within a greater port area to another. Average


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maneuvering speeds vary from 3 to 8 knots depending on direction and ship type. For consistency, maneuvering speeds were assumed to be the dead slow setting of approximately 5.8 knots.

Load factor during maneuvering is calculated as follows:

Equation 3-15 ( )3][ ][/][ knotsSpeedMaximumknotsSpeedManLoadFactor mainman =

In addition:

Equation 3-16 94.0/][knotsSpeedCruiseSpeedMaximum =


Also, the maneuvering speed is 5.8 knots. Substituting Equation 3-16 into Equation 3-15, and using a maneuvering speed of 5.8 knots, the equation to calculate load factor becomes:

Equation 3-17 ( )3][ /45.5 SpeedCruiseLoadFactor mainman =

Load factors below 2 percent were set to 2 percent as a minimum.

Substituting Equation 3-17 for load factor into Equation 3-14, the expression used to

calculate maneuvering mode emissions for the main engines becomes:

Equation 3-18 Maneuvering Mode Emissions for Main Engines )/10()()()/45.5()/()()( 6

][3

][][ gtonnesAdjEFSpeedCruisecallhrsPcallsEmissions mainmanmainmainman−××××××=

Where: Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[main] = Total main engine power, in kilowatts hrs/callman = Hours per call for maneuvering mode Cruise speed = Vessel service speed, in knots EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these


Since the load factor during maneuvering usually falls below 20 percent, low load

adjustment factors are also applied accordingly. Maneuvering times are not readily available for all 117 ports. For this analysis, maneuvering times and load factors available for a subset of the ports were used to calculate maneuvering emissions for the remaining ports. This is discussed in more detail in Section 3.3.2.3.8.


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The equation used to calculate maneuvering mode emissions for the auxiliary engines is:

Equation 3-19 Maneuvering Mode Emissions for Auxiliary Engines )/10()()()/()()( 6

][][][][ gtonnesEFLFcallhrsPcallsEmissions auxauxmanmanauxauxman−×××××=

Where: Emissionsman[aux] = Metric tonnes emitted from auxiliary engines in maneuvering mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[aux] = Total auxiliary engine power, in kilowatts hrs/callman = Hours per call for maneuvering mode LFman [aux] = Load factor for auxiliary engines in maneuvering mode, unitless (these vary by

ship type and activity mode) EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these


Low load adjustment factors are not applied for auxiliary engines.

3.3.2.2.4 Hotelling

Hotelling emissions are calculated for auxiliary engines only, as main engines are not operational during this mode. The equation used to calculate hotelling mode emissions for the auxiliary engines is:

Equation 3-20 Hotelling Mode Emissions for Auxiliary Engines

)/10()()()/()()( 6][][][][ gtonnesEFLFcallhrsPcallsEmissions auxauxhotelhotelauxauxhotel

−×××××=

Where: Emissionshotel[aux] = Metric tonnes emitted from auxiliary engines in hotelling mode calls = Round-trip visits (i.e., one entrance and one clearance is considered a call) P[aux] = Total auxiliary engine power, in kilowatts hrs/callhotel = Hours per call for hotelling mode LFhotel [aux] = Load factor for auxiliary engines in hotelling mode, unitless (these vary by ship

type and activity mode) EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these


Hotelling times are not readily available for all 117 ports. For this analysis, hotelling

times available for a subset of the ports were used to calculate hotelling emissions for the remaining ports. This is discussed in more detail in Section 3.3.2.3.8.

3.3.2.3 Inputs for Port Emission Calculations

From a review of the equations described in Section 3.3.2.2, the following inputs are required to calculate emissions for the four modes of operation (cruise, RSZ, maneuvering, and hotelling):


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• Number of calls • Main engine power • Cruise (vessel service) speed • Cruise distance • RSZ distance for each port • RSZ speed for each port • Auxiliary engine power • Auxiliary load factors • Main and auxiliary emission factors • Low load adjustment factors for main engines • Maneuvering time-in-mode (hours/call) • Hotelling time-in-mode (hours/call)

Note that load factors for main engines are not listed explicitly, since they are calculated

as a function of mode and/or cruise speed. This section describes the inputs in more detail, as well as the sources for each input.

3.3.2.3.1 Calls and Ship Characteristics (Propulsion Engine Power and Cruise Speed)

For this analysis, U.S. Army Corps of Engineers (USACE) entrance and clearance data for 2002,13 together with Lloyd’s data for ship characteristics,14 were used to calculate average ship characteristics and calls by ship type for each port. Information for number of calls, propulsion engine power, and cruise speed were obtained from these data.

3.3.2.3.1.1 Bins by Ship Type, Engine Type, and DWT Range

The records from the USACE entrances and clearances data base were matched with Lloyd’s data on ship characteristics for each port. Calls by vessels that have either Category 1 or 2 propulsion engines were eliminated from the data set. The data was then binned by ship type, engine type and dead weight tonnage (DWT) range. The number of entrances and clearances in each bin are counted, summed together and divided by two to determine the number of calls (i.e., one entrance and one clearance was considered a call). For Great Lake ports, there is a larger frequency of ships either entering the port loaded and leaving unloaded (light) or entering the port light and leaving loaded. In these cases, there would only be one record (the loaded trip into or out of the port) that would be present in the data. For Great Lake ports, clearances were matched with entrances by ship name. If there was not a reasonable match, the orphan entrance or clearance was treated as a call.

Propulsion power and vessel cruise speed are also averaged for each bin. While each port is analyzed separately, the various bins and national average ship characteristics are given in Table 3-1 for deep sea ports and Table 3-2 for Great Lake ports. Auxiliary engine power was computed from the average propulsion power using the auxiliary power to propulsion power ratios discussed in Section 3.3.2.3.4.


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Table 3-1 Bins and Average Ship Characteristics for Deep Sea Ports

Engine Power (kW) Ship Type Main

Engine a DWT Range Calls Main Auxiliary

Cruise Speed (kts)

DWT

< 10,000 35 6,527 1,736 16.0 6,211 10,000 – 20,000 224 10,499 2,793 18.2 13,003 MSD 20,000 – 30,000 28 6,620 1,761 13.0 22,268

MSD Total 286 9,640 2,564 17.4 13,063 <10,000 84 7,927 2,109 17.7 8,845 10,000 – 20,000 2,316 10,899 2,899 18.7 14,959 SSD 20,000 – 30,000 621 13,239 3,522 19.5 24,860

AUTO CARRIER

SSD Total 3,020 11,298 3,005 18.8 16,826 AUTO CARRIER Total 3,306 11,155 2,967 18.7 16,500

MSD < 25,000 1 4,461 1,200 13.3 4,393 MSD Total 1 4,461 1,200 13.3 4,393

< 25,000 1 3,916 1,053 14.0 11,783 35,000 – 45,000 20 19,463 5,236 18.0 44,799 SSD 45,000 – 90,000 19 25,041 6,736 20.0 48,093

SSD Total 40 21,724 5,844 18.9 45,538 ST 35,000 – 45,000 5 24,196 6,509 21.7 41,294

BARGE CARRIER

ST Total 5 24,196 6,509 21.7 41,294 BARGE CARRIER Total 45 21,779 5,859 19.1 44,657

< 25,000 213 4,867 1,080 14.0 15,819 25,000 – 35,000 6 8,948 1,986 14.0 29,984 35,000 – 45,000 44 9,148 2,031 15.2 39,128 45,000 – 90,000 51 9,705 2,155 14.3 71,242

MSD

> 90,000 1 16,109 3,576 15.8 105,550

BULK CARRIER

MSD Total 314 6,360 1,412 14.2 28,621


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Table 3-1 Bins and Average Ship Characteristics for Deep Sea Ports (continued) Engine Power (kW)

Ship Type Main Engine a DWT Range Calls

Main Auxiliary

Cruise Speed (kts)

DWT

< 25,000 1,194 5,650 1,254 14.2 19,913 25,000 – 35,000 2,192 7,191 1,596 14.6 29,323 35,000 – 45,000 1,742 8,515 1,890 14.7 39,875 45,000 – 90,000 3,733 9,484 2,105 14.4 62,573

SSD

> 90,000 352 14,071 3,124 14.5 112,396 SSD Total 9,212 8,434 1,872 14.5 46,746

< 25,000 72 6,290 1,396 15.0 18,314 ST

25,000 – 35,000 3 8,948 1,986 15.0 33,373

BULK CARRIER

ST Total 75 6,379 1,416 15.0 18,819 BULK CARRIER Total 9,600 8,350 1,854 14.5 45,936

< 25,000 1,005 6,846 1,506 17.2 8,638 25,000 – 35,000 53 22,304 4,907 20.6 28,500 35,000 – 45,000 59 26,102 5,742 22.3 39,932

MSD

45,000 – 90,000 248 37,650 8,283 24.0 56,264 MSD Total 1,365 13,878 3,053 18.8 19,419

< 25,000 2,054 12,381 2,724 19.1 18,776 25,000 – 35,000 2,360 19,247 4,234 20.5 31,205 35,000 – 45,000 2,443 24,755 5,446 21.8 40,765 45,000 – 90,000 6,209 36,151 7,953 23.3 58,604

SSD

> 90,000 98 57,325 12,612 25.0 105,231 SSD Total 13,163 27,454 6,040 21.9 44,513

< 25,000 46 20,396 4,487 20.8 19,963 25,000 – 35,000 89 21,066 4,635 21.0 30,804 ST 35,000 – 45,000 41 23,562 5,184 21.0 40,949

CONTAINER SHIP

ST Total 176 21,472 4,724 21.0 30,334 CONTAINER SHIP Total 14,703 26,122 5,747 21.6 42,014

< 25,000 2,937 5,080 1,316 15.1 8,268 25,000 – 35,000 38 9,458 2,450 15.4 30,746 35,000 – 45,000 1 13,728 3,556 14.3 40,910

MSD

45,000 – 90,000 9 11,932 3,090 16.0 50,250 MSD Total 2,984 5,159 1,336 15.1 8,688

< 25,000 2,357 6,726 1,742 15.4 14,409 25,000 – 35,000 500 7,575 1,962 14.9 29,713 35,000 – 45,000 1,122 9,269 2,401 15.2 41,568 45,000 – 90,000 405 9,336 2,418 15.1 47,712

SSD

> 90,000 6 10,628 2,753 14.5 134,981 SSD Total 4,389 7,718 1,999 15.3 26,326 ST < 25,000 18 17,897 4,635 21.0 22,548

GENERAL CARGO

ST Total 18 17,897 4,635 21.0 22,548 GENERAL CARGO Total 7,391 6,709 1,738 15.2 19,196


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Main Auxiliary

Cruise Speed (kts)

DWT

MSD All 51 9,405 2,530 12.7 6,083 MSD Total 51 9,405 2,530 12.7 6,083 MSD-ED All 6 16,968 4,565 12.7 15,795 MSD-ED Total 6 16,968 4,565 12.7 15,795 SSD All 7 4,659 1,253 14.2 8,840 SSD Total 7 4,659 1,253 14.2 8,840 ST All 1 12,871 3,462 21.0 16,605

MISCELLANEOUS

ST Total 1 12,871 3,462 21.0 16,605 MISCELLANEOUS Total 64 9,564 2,573 13.0 7,311

<10,000 1,011 22,024 6,123 20.2 5,976 MSD

10,000 - 20,000 24 96,945 26,951 28.5 15,521 MSD Total 1,035 23,762 6,606 20.4 6,197

<10,000 1,964 39,095 10,868 20.9 7,345 MSD-ED

10,000 - 20,000 228 53,236 14,800 22.0 10,924 MSD-ED Total 2,192 40,566 11,277 21.1 7,717 SSD <10,000 189 23,595 6,559 20.1 6,235 SSD Total 189 23,595 6,559 20.1 6,235 GT-ED 10,000 - 20,000 143 44,428 12,351 24.0 11,511 GT-ED Total 143 44,428 12,351 24.0 11,511

<10,000 13 16,858 4,687 21.2 6,981 ST

10,000 - 20,000 52 29,982 8,335 18.0 13,960

PASSENGER

ST Total 65 27,357 7,605 18.6 12,564 PASSENGER Total 3,623 34,800 9,674 20.9 7,443

<10,000 122 4,829 1,961 16.3 5,646 MSD

10,000 - 20,000 60 12,506 5,077 20.0 11,632 MSD Total 182 7,360 2,988 17.5 7,619

<10,000 464 6,539 2,655 18.0 7,267 SSD

10,000 - 20,000 801 12,711 5,161 20.8 13,138

REEFER

SSD Total 1,265 10,449 4,242 19.7 10,986 REEFER Total 1,447 10,060 4,084 19.5 10,562

<10,000 892 7,840 2,031 15.5 6,641 10,000 - 20,000 286 9,312 2,412 17.0 11,338 MSD > 30,000 31 22,386 5,798 21.0 31,508

MSD Total 1,208 8,561 2,217 16.0 8,389 <10,000 132 7,240 1,875 15.0 4,695 10,000 - 20,000 208 9,062 2,347 16.9 14,293 20,000 - 30,000 31 12,781 3,310 18.9 22,146

SSD

> 30,000 555 20,362 5,274 18.9 42,867 SSD Total 925 15,702 4,067 17.9 30,321 GT > 30,000 1 47,076 12,193 24.0 36,827 GT Total 1 47,076 12,193 24.0 36,827

10,000 – 20,000 2 22,373 5,795 25.0 16,144 ST

20,000 – 30,000 1 22,373 5,795 25.0 22,501

RORO

ST Total 3 22,373 5,795 25.0 18,687 RORO Total 2,137 11,687 3,027 16.8 17,910


3-18



Main Auxiliary

Cruise Speed (kts)

DWT

<30,000 650 4,888 1,031 14.3 11,415 30,000 - 60,000 181 10,533 2,222 15.3 42,153 60,000 - 90,000 148 9,782 2,064 14.7 74,245

MSD

90,000 - 120,000 3 15,139 3,194 14.1 113,957 MSD Total 981 6,697 1,413 14.6 26,847

<30,000 3,050 6,303 1,330 14.6 17,145 30,000 - 60,000 3,752 9,021 1,903 14.9 41,677 60,000 - 90,000 1,766 10,310 2,175 14.6 74,595 90,000 - 120,000 2,835 12,318 2,599 14.6 101,116 120,000 - 150,000 258 15,840 3,342 14.7 144,405

SSD

> 150,000 487 16,888 3,563 15.2 166,394 SSD Total 12,147 9,755 2,058 14.7 61,353 GT-ED 30,000 - 60,000 13 7,592 1,602 14.5 39,839 GT-ED Total 13 7,592 1,602 14.5 39,839

< 30,000 2 13,534 2,856 18.0 27,235 30,000 - 60,000 87 15,818 3,338 17.9 43,982 60,000 - 90,000 73 26,848 5,665 18.9 70,108 90,000 - 120,000 4 17,660 3,726 16.3 91,868 120,000 - 150,000 3 19,125 4,035 16.0 122,409

ST

> 150,000 2 20,785 4,386 14.3 190,111

TANKER

ST Total 170 20,678 4,363 18.2 58,616 TANKER Total 13,310 9,667 2,040 14.8 58,754

MSD All 48 7,579 2,039 14.5 626 TUG

MSD Total 48 7,579 2,039 14.5 626 TUG Total 48 7,579 2,039 14.5 626 Grand Total 55,672 15,212 3,593 17.4 38,083 Note: a Engine Types: MSD = medium speed engine; SSD = slow speed engine; ST = steam turbine; GT = gas turbine


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Table 3-2 Bins and Average Ship Characteristics for Great Lake Ports Engine Power (kW)

Ship Type Main Engine a DWT Range Calls Main Auxiliary

Cruise Speed (kts) DWT

10,000 - 20,000 9 4,413 980 15.3 11,693 20,000 - 30,000 4 8,826 1,959 14.0 28,481 MSD 30,000 - 40,000 11 6,001 1,332 13.5 32,713

MSD Total 24 5,876 1,305 14.2 24,125 10,000 - 20,000 18 4,844 1,075 13.6 14,392 20,000 - 30,000 208 6,995 1,553 14.6 27,486 SSD 30,000 - 40,000 223 8,284 1,839 14.1 34,172

SSD Total 449 7,549 1,676 14.3 30,282 ST 20,000 - 30,000 23 6,910 1,534 15.5 26,513

BULK CARRIER

ST Total 23 6,910 1,534 15.5 26,513 BULK CARRIER Total 496 7,438 1,651 14.4 29,809

10,000 - 20,000 5 3,114 691 10.5 12,513 20,000 - 30,000 12 6,436 1,429 15.0 28,591 30,000 - 40,000 771 6,881 1,528 13.2 33,531

MSD

> 40,000 67 12,140 2,695 13.5 65,089 MSD Total 855 7,265 1,613 13.3 35,812

20,000 - 30,000 275 6,659 1,478 15.0 26,504 SSD

30,000 - 40,000 122 7,574 1,681 14.9 34,476 SSD Total 397 6,940 1,541 14.9 28,954

< 10,000 26 3,236 718 12.3 4,538 10,000 - 20,000 93 4,750 1,055 13.6 16,830 ST 20,000 - 30,000 79 6,679 1,483 16.6 28,847

SELF UNLOADING BULK CARRIER

ST Total 198 5,321 1,181 14.6 20,011 SELF UNLOADING BULK CARRIER Total 1,450 6,910 1,534 13.9 31,776

< 10,000 87 4,436 847 15.1 6,755 MSD

10,000 - 20,000 6 5,939 1,134 16.5 12,497 MSD Total 93 4,533 866 15.2 7,125

< 10,000 3 4,763 910 16.4 6,708 10,000 - 20,000 7 6,280 1,199 14.1 16,993 20,000 - 30,000 1 7,099 1,356 16.0 24,432

SSD

30,000 - 40,000 6 8,827 1,686 15.0 30,900

GENERAL CARGO

SSD Total 17 6,959 1,329 14.9 20,524 GENERAL CARGO Total 110 4,908 937 15.1 9,196

MSD All 24 5,364 1,443 13.8 672 INTEGRATED TUG-BARGE MSD Total 24 5,364 1,443 13.8 672 INTEGRATED TUG-BARGE Total 24 5,364 1,443 13.8 672

MSD 10,000 - 20,000 42 3,972 838 13.5 10,475 MSD Total 42 3,972 838 13.5 10,475 SSD 10,000 - 20,000 5 5,160 1,089 14.3 13,735

TANKER

SSD Total 5 5,160 1,089 14.3 13,735 TANKER Total 47 4,098 865 13.6 10,822 Grand Total 2,127 6,850 1,515 14.1 29,336 Note: a Engine Types: MSD = medium speed engine; SSD = slow speed engine; ST = steam turbine


3-20

3.3.2.3.1.2 Removal of Category 1 and 2 Ships

Since these inventories were intended to cover ships with Category 3 propulsion engines only, the ships with Category 1 and 2 propulsion engines were eliminated. This was accomplished by matching all ship calls with information from Lloyd’s Data, which is produced by Lloyd’s Register-Fairplay Ltd.14 Over 99.9 percent of the calls in the entrances and clearances data were directly matched with Lloyd’s data. The remaining 0.1 percent was estimated based upon ships of similar type and size.

Engine category was determined from engine make and model. Engine bore and stroke were found in the Marine Engine 2005 Guide15 and displacement per cylinder was calculated. Ships with Category 1 or 2 propulsion engines were eliminated from the data.

Many passenger ships and tankers have either diesel-electric or gas turbine-electric engines that are used for both propulsion and auxiliary purposes. Both were included in the current inventory.

3.3.2.3.1.3 Treatment of Electric-Drive Ships

Many passenger ships and tankers have either diesel-electric or gas turbine-electric engines that are used for both propulsion and auxiliary purposes. Both were included in the current inventory.

Lloyds clearly calls out these types of engines in their database and that information was used to distinguish them from direct and geared drive systems. Generally the power Lloyds lists is the total power. To separate out propulsion from auxiliary power for purposes of calculating emissions, the total power listed in the Lloyds data was divided by 1 plus the ratio of auxiliary to propulsion power (given in Table 3-3) to obtain the propulsion power portion of the total. The remaining portion was considered auxiliary engine power. In addition, no low load adjustment factor was applied to diesel and gas turbine electric engines for loads below 20 percent MCR because several engines are used to generate power, and some can be shut down to allow others to operate at a more efficient setting.

3.3.2.3.2 Cruise Distance

Cruise mode emissions are calculated assuming a 25 nautical mile distance into and out of the port for deep sea ports and 7 nautical miles into and out of the port for Great Lake ports outside of the reduced speed and maneuvering zones.

3.3.2.3.3 RSZ Distances and Speeds by Port

Reduced speed zone (RSZ) distance and speed were determined for each port. For deep sea ports, the RSZ distances were developed from shipping lane information contained in the U.S. Army Corps of Engineers National Waterway Network.16 The NWN is a geographic database of navigable waterways in and around the U.S. The database defines waterways as links or line segments that, for the purposes of this study, represent actual shipping lanes (i.e.,


3-21

channels, intracoastal waterways, sea lanes, and rivers). The geographic locations of the waterways that were directly associated with each of the 117 ports were viewed using geographic information system computer software. The sea-side endpoint for the RSZ was selected as the point along the line segment that was judged to be far enough into the ocean where ship movements were unconstrained by the coastline or other vessel traffic. These RSZ sea-side endpoints typically coincided with estimates provided by the pilots for the major ports as reported in earlier work. The resulting RSZ distance was then measured for each deep sea port. The final RSZ distances and endpoints for each port are listed in the Appendix, Table 3-103. The RSZ for each Great Lake port was fixed at three nautical miles, as previously discussed in Section 3.3.2.2.2.

The RSZ speeds were primarily taken from previous studies by ICF17,18 or from an ENVIRON report19 based upon discussions with pilots. A few of the RSZ speeds were also modified based upon newer information obtained from conversations with pilots. The final RSZ speeds for each port are listed in the Appendix, Table 3-103. The RSZ speeds for the Great Lake ports vary by vessel type and are the average of the vessel service speed and the maneuvering speed.

3.3.2.3.4 Auxiliary Engine Power and Load Factors

Since hotelling emissions are a large part of port inventories, it is important to distinguish propulsion engine emissions from auxiliary engine emissions. In the methodology used in this analysis, auxiliary engine maximum continuous rating power and load factors were calculated separately from propulsion engines and different emission factors (EFs) applied. All auxiliary engines were treated as Category 2 medium-speed diesel (MSD) engines for purposes of this analysis.

Auxiliary engine power is not contained in the USACE database and is only sparsely populated in the Lloyd’s database; as a result, it must be estimated. The approach taken was to derive ratios of average auxiliary engine power to propulsion power based on survey data. The California Air Resources Board (ARB) conducted an Oceangoing Ship Survey of 327 ships in January 2005 that was principally used for this analysis.20 Average auxiliary engine power to propulsion power ratios were estimated by ship type and are presented in Table 3-3. These ratios by ship type were applied to the propulsion power data to derive auxiliary power for the ship types at each port.


3-22

Table 3-3 Auxiliary Engine Power Ratios (ARB Survey, except as noted)

Average Auxiliary Engines

Ship Type

Average Propulsion

Engine (kW) Number

Power Each (kW)

Total Power (kW) Engine Speed

Auxiliary to Propulsion

Ratio

Auto Carrier 10,700 2.9 983 2,850 Medium 0.266

Bulk Carrier 8,000 2.9 612 1,776 Medium 0.222

Container Ship 30,900 3.6 1,889 6,800 Medium 0.220

Passenger Shipa 39,600 4.7 2,340 11,000 Medium 0.278

General Cargo 9,300 2.9 612 1,776 Medium 0.191

Miscellaneousb 6,250 2.9 580 1,680 Medium 0.269

RORO 11,000 2.9 983 2,850 Medium 0.259

Reefer 9,600 4.0 975 3,900 Medium 0.406

Tanker 9,400 2.7 735 1,985 Medium 0.211 Notes: a Many passenger ships typically use a different engine configuration known as diesel-electric. These vessels use large generator sets for both propulsion and ship-board electricity. The figures for passenger ships above are estimates taken from the Starcrest Vessel Boarding Program. b Miscellaneous ship types were not provided in the ARB methodology, so values from the Starcrest Vessel Boarding Program were used.

Load factors for auxiliary engines vary by ship type and operating mode. It was

previously thought that power generation was provided by propulsion engines in all modes but hotelling. Starcrest’s Vessel Boarding Program12 showed that auxiliary engines are on all of the time, except when using shoreside power during hotelling. Table 3-4 shows the auxiliary engine load factors by ship type determined by Starcrest, through interviews conducted with ship captains, chief engineers, and pilots during its vessel boarding programs. Auxiliary load factors were used in conjunction with total auxiliary power. Auxiliary load factors listed in Table 3-4 are used together with the total auxiliary engine power (determined from total propulsion power and the ratios from Table 3-3) to calculate auxiliary engine emissions.


3-23

Table 3-4 Auxiliary Engine Load Factor Assumptions

Ship-Type Cruise RSZ Maneuver Hotel

Auto Carrier 0.13 0.30 0.67 0.24

Bulk Carrier 0.17 0.27 0.45 0.22

Container Ship 0.13 0.25 0.50 0.17

Passenger Ship 0.80 0.80 0.80 0.64

General Cargo 0.17 0.27 0.45 0.22

Miscellaneous 0.17 0.27 0.45 0.22

RORO 0.15 0.30 0.45 0.30

Reefer 0.20 0.34 0.67 0.34

Tanker 0.13 0.27 0.45 0.67

3.3.2.3.5 Fuel Types and Fuel Sulfur Levels

There are primarily three types of fuel used by marine engines: residual marine (RM), marine diesel oil (MDO), and marine gas oil (MGO), with varying levels of fuel sulfur.5 MDO and MGO are generally described as distillate fuels. For this analysis, RM and MDO fuels are assumed to be used. Since PM and SO2 emission factors are dependent on the fuel sulfur level, calculation of port inventories requires information about the fuel sulfur levels associated with each fuel type, as well as which fuel types are used by propulsion and auxiliary engines.

An ARB survey20 found that almost all ships used RM in their main propulsion engines, and that only 29 percent of all ships (except passenger ships) used distillate in their auxiliary engines, with the remaining 71 percent using RM. However, only 8 percent of passenger ships used distillate in their auxiliary engines, while the other 92 percent used RM. We used the results of this survey as reasonable approximations for calculations of emission factors. However, their accuracy for years other than those of the ARB survey may be affected by fuel prices, since as fuel prices increase, more ships will use RM in their auxiliary engines.

Based on the ARB survey, average fuel sulfur level for residual marine was set to 2.5 percent for the west coast and 2.7 percent for the rest of the country. A sulfur content of 1.5 percent was used for MDO.21 While a more realistic value for MDO used in the U.S. appears to be 0.4 percent, given the small proportion of distillate fuel used by ships relative to RM, the difference should not be significant. Sulfur levels in other areas of the world can be significantly higher for RM. Table 3-5 provides the assumed mix of fuel types used for propulsion and auxiliary engines by ship type.


3-24

Table 3-5 Estimated Mix of Fuel Types Used by Ships

Fuel Used

Ship Type Propulsion Auxiliary

Passenger 100% RM 92% RM/8% MDO

Other 100% RM 71% RM/29% MDO

3.3.2.3.6 Propulsion and Auxiliary Engine Emission Factors

An analysis of emission data was prepared and published in 2002 by Entec.21 The resulting Entec emission factors include individual factors for three speeds of diesel engines (slow-speed diesel (SSD), medium-speed diesel (MSD), and high-speed diesel (HSD)), steam turbines (ST), gas turbines (GT), and two types of fuel used here (RM and MDO). Table 3-6 lists the propulsion engine emission factors for NOX and HC that were used for the 2002 port inventory development. The CO, PM, SO2 and CO2 emission factors shown in the table come from other data sources as explained below.

Table 3-6 Emission Factors for OGV Main Engines using RM, g/kWh

All Ports West Coast Ports Other Ports

Engine NOX CO HC CO2 PM10 PM2.5 SO2 PM10 PM2.5

SO2

SSD 18.1 1.40 0.60 620.62 1.4 1.3 9.53 1.4 1.3 10.29

MSD 14.0 1.10 0.50 668.36 1.4 1.3 10.26 1.4 1.3 11.09

ST 2.1 0.20 0.10 970.71 1.4 1.3 14.91 1.5 1.4 16.10

GT 6.1 0.20 0.10 970.71 1.4 1.3 14.91 1.5 1.4 16.10

CO emission factors were developed from information provided in the Entec appendices because they are not explicitly stated in the text. . HC and CO emission factors were confirmed with a recent EPA review.22

PM10 values were determined by EPA based on existing engine test data in consultation with ARB.23 GT PM10 emission factors were not part of the EPA analysis but assumed here to be equivalent to ST PM10 emission factors. Test data shows PM10 emission rates as dependent upon fuel sulfur levels, with base PM10 emission rates of 0.23 g/kw-hr with distillate fuel (0.24% sulfur) and 1.35 g/kw-hr with residual fuel (2.46% sulfur).24 The equation used to generate emission factors based on sulfur content is shown below.

Equation 3-21 Calculation of PM10 Emission Factors Based on Fuel Sulfur Levels

PMEF = PMNom + [(SAct – SNom) × BSFC × FSC × MWR × 0.0001] Where: PMEF = PM emission factor adjusted for fuel sulfur PMNom = PM emission rate at nominal fuel sulfur level = 0.23 g/kW-hr for distillate fuel, 1.35 g/kW-hr for residual fuel


3-25

SAct = Actual fuel sulfur level (weight percent) SNom = nominal fuel sulfur level (weight percent) = 0.24 for distillate fuel, 2.46 for residual fuel BSFC = fuel consumption in g/kW-hr

= 200 g/kW-hr used for this analysis FSC = percentage of sulfur in fuel that is converted to direct sulfate PM = 2.247% used for this analysis MWR = molecular weight ratio of sulfate PM to sulfur

= 224/32 = 7 used for this analysis The PM10 to PM2.5 conversion factor used here is 0.92. While the NONROAD model

uses 0.97 for such conversion based upon low sulfur fuels, a reasonable value seems to be closer to 0.92 because higher sulfur fuels in medium and slow speed engines would tend to produce larger particulates than high speed engines on low sulfur fuels.

SO2 emission factors were based upon a fuel sulfur to SO2 conversion formula which was supplied by ENVIRON.25 Emission factors for SO2 emissions were calculated using the formula assuming that 97.753 percent of the fuel sulfur was converted to SO2.26 The brake specific fuel consumption (BSFC)A that was used for SSDs was 195 g/kWh, while the BSFC that was used for MSDs was 210 g/kWh based upon Lloyds 1995. The BSFC that was used for STs and GTs was 305 g/kWh based upon Entec.21

Equation 3-22 Calculation of SO2 Emission Factors, g/kWh SO2 EF = BSFC x 2 x 0.97753 x Fuel Sulfur Fraction

CO2 emission factors were calculated from the BSFC assuming a fuel carbon content of

86.7 percent by weight21 and a ratio of molecular weights of CO2 and C at 3.667.

Equation 3-23 Calculation of CO2 Emission Factors, g/kWh CO2 EF = BSFC x 3.667 x 0.867

Fuel consumption was calculated from CO2 emissions based on a 1:3.183 ratio. 3.183

tons of CO2 emissions are assumed produced from one metric ton of fuel. The most current set of auxiliary engine emission factors comes from Entec except as

noted below. Table 3-7 provides these auxiliary engine emission factors.

Table 3-7 Auxiliary Engine Emission Factors by Fuel Type, g/kWh


Engine Fuel NOX CO HC CO2 PM10 PM2.5 SO2 PM10 PM2.5

SO2

RM 14.70 1.10 0.40 668.36 1.4 1.3 10.26 1.4 1.3 11.09 MSD MDO 13.90 1.10 0.40 668.36 0.6 0.55 6.16 0.6 0.55 6.16

A Brake specific fuel consumption is sometimes called specific fuel oil consumption (SFOC).


3-26

It should be noted that Entec used 2.7 percent fuel sulfur content for RM, and 1.0 percent for MDO which is consistent with the RM assumptions made in this analysis for other than West Coast ports. For MDO, there is a slight discrepancy between the 1.0 percent used by Entec versus the 1.5 percent estimate used for this analysis. SO2 emission factors were calculated based upon the assumed sulfur levels and the methodology suggested by ENVIRON25 while PM emissions were determined by EPA based on existing engine test data in consultation with ARB.23

Using the ratios of RM versus MDO use determined by the ARB study20 as given in

Table 3-5 together with the emission factors shown in Table 3-7, the auxiliary engine emission factor averages by ship type are listed in Table 3-8. As discussed above, this fuel sulfur level may be too high for the U.S. However, we do not believe this emission factor has a significant effect on the total emission inventory estimates.

If the fuel sulfur level for MDO is correctly adjusted from 1.5 percent to 1.0 percent, the effect on SO2 emissions is still less than 7 percent, due to the high percentage of RM fuel used in auxiliary engines. The difference for PM is within the round off error of the emission factor.

Table 3-8 Auxiliary Engine Emission Factors by Ship Type, g/kWh


Ship Type NOX CO HC CO2 PM10 PM2.5 SO2 PM10 PM2.5

SO2

Passenger 14.64 1.10 0.40 668.36 1.3 1.2 9.93 1.4 1.3 10.70

Others 14.47 1.10 0.40 668.36 1.1 1.0 9.07 1.2 1.1 9.66

3.3.2.3.7 Low Load Adjustment Factors for Propulsion Engines

Emission factors are considered to be constant down to about 20 percent load. Below that threshold, emission factors tend to increase as the load decreases. This trend results because diesel engines are less efficient at low loads and the BSFC tends to increase. Thus, while mass emissions (grams per hour) decrease with low loads, the engine power tends to decrease more quickly, thereby increasing the emission factor (grams per engine power) as load decreases. Energy and Environmental Analysis Inc. (EEA) demonstrated this effect in a study prepared for EPA in 2000.27 In the EEA report, various equations have been developed for the various emissions. The low-load emission factor adjustment factors were developed based upon the concept that the BSFC increases as load decreases below about 20 percent load. For fuel consumption, EEA developed the following equation:

Equation 3-24 Fuel Consumption (g/kWh) = 14.1205 (1/Fractional Load) + 205.7169

In addition, based upon test data, they developed algorithms to calculate emission factors

at reduced load. These equations are noted below:


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Equation 3-25

Emission Rate (g/kWh) = a (Fractional Load)-x + b

For SO2 emissions, however, EEA developed a slightly different equation:

Equation 3-26 Emission Rate (g/kWh) = a (Fuel Consumption x Fuel Sulfur Fraction) + b

The coefficients for the above equations are given in Table 3-9 below.

Table 3-9 Emission Factor Algorithm Coefficients for OGV Main Engines using RM Coefficient NOX HC CO PM SO2 CO2

a 0.1255 0.0667 0.8378 0.0059 2.3735 44.1 x 1.5 1.5 1.0 1.5 n/a 1.0 b 10.4496 0.3859 0.1548 0.2551 -0.4792 648.6

The underlying database used to calculate these coefficients includes primarily tests on engines rated below 10,000 kW, using diesel fuel. This introduces uncertainty regarding the use of these coefficients for Category 3 engines using residual fuel; however, these are the best estimates currently available.

Using these algorithms, fuel consumption and emission factors versus load were calculated. By normalizing these emission factors to 20% load, the low-load multiplicative adjustment factors presented in Table 3-10 are calculated. SO2 adjustment factors were calculated using 2.7% sulfur. The SO2 multiplicative adjustment factors at 2.5 percent sulfur are not significantly different.


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Table 3-10 Calculated Low Load Multiplicative Adjustment Factors

Load (%) NOX HC CO PM SO2 CO2 1 11.47 59.28 19.32 19.17 5.99 5.82 2 4.63 21.18 9.68 7.29 3.36 3.28 3 2.92 11.68 6.46 4.33 2.49 2.44 4 2.21 7.71 4.86 3.09 2.05 2.01 5 1.83 5.61 3.89 2.44 1.79 1.76 6 1.60 4.35 3.25 2.04 1.61 1.59 7 1.45 3.52 2.79 1.79 1.49 1.47 8 1.35 2.95 2.45 1.61 1.39 1.38 9 1.27 2.52 2.18 1.48 1.32 1.31

10 1.22 2.20 1.96 1.38 1.26 1.25 11 1.17 1.96 1.79 1.30 1.21 1.21 12 1.14 1.76 1.64 1.24 1.18 1.17 13 1.11 1.60 1.52 1.19 1.14 1.14 14 1.08 1.47 1.41 1.15 1.11 1.11 15 1.06 1.36 1.32 1.11 1.09 1.08 16 1.05 1.26 1.24 1.08 1.07 1.06 17 1.03 1.18 1.17 1.06 1.05 1.04 18 1.02 1.11 1.11 1.04 1.03 1.03 19 1.01 1.05 1.05 1.02 1.01 1.01 20 1.00 1.00 1.00 1.00 1.00 1.00

There is no need for a low load adjustment factor for auxiliary engines, because they are

generally operated in banks. When only low loads are needed, one or more engines are shut off, allowing the remaining engines to operate at an efficient level.

3.3.2.3.8 Use of Detailed Typical Port Data for Other Inputs

There is currently not enough information to readily calculate time-in-mode (hours/call) for all 117 ports during the maneuvering and hotelling modes of operation. As a result, it was necessary to review and select available detailed emission inventories that have been estimated for selected ports to date. These ports are referred to as typical ports. The typical port information for maneuvering and hotelling time-in-mode (as well as maneuvering load factors for the propulsion engines) was then used for the typical ports and also assigned to the other modeled ports. A modeled port is the port in which emissions are to be estimated. The methodology that was used to select the typical ports and match these ports to the other modeled ports is briefly described in this section, and more fully described in the ICF documentation.2

3.3.2.3.8.1 Selection of Typical Ports

In 1999, EPA published two guidance documents17,18 to calculate marine vessel activity at ports. These documents contained detailed port inventories of eight deep sea ports, two Great


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Lake ports and two inland river ports. The detailed inventories were developed by obtaining ship call data from Marine Exchanges/Port Authorities (MEPA) at the various ports for 1996 and matching the various ship calls to data from Lloyds Maritime Information Services to provide ship characteristics. The ports for which detailed inventories were developed are shown in Table 3-11 for deep sea ports and Table 3-12 for Great Lake ports along with the level of detail of shifts for each port. Most ports provided the ship name, Lloyd’s number, the vessel type, the date and time the vessel entered and left the port, and the vessel flag. Inland river ports were developed from U.S. Army Corps of Engineers (USACE) Waterborne Commerce Statistics Center data.

Table 3-11 Deep Sea MEPA Vessel Movement and Shifting Details MEPA Area and Ports MEPA Data Includes

Lower Mississippi River including the ports of New Orleans, South Louisiana, Plaquemines, and Baton Rouge

Information on the first and last pier/wharf/dock (PWD) for the vessel (gives information for at most one shift per vessel). No information on intermediate PWDs, the time of arrival at the first destination PWD, or the time of departure from the River.

Consolidated Port of New York and New Jersey and other ports on the Hudson and Elizabeth Rivers

All PWDs or anchorages for shifting are named. Shifting arrival and departure times are not given. Hotelling time is based upon the entrance and clearance times and dates, subtracting out maneuvering times. Maneuvering times were calculated based upon the distance the ship traveled at a given maneuvering speed.

Delaware River Ports including the ports of Philadelphia, Camden, Wilmington and others


Puget Sound Area Ports including the ports of Seattle, Tacoma, Olympia, Bellingham, Anacortes, and Grays Harbor

All PWDs or anchorages for shifting are named. Arrival and departure dates and times are noted for all movements, allowing calculation of maneuvering and hotelling both for individual shifts and the overall call on port.

The Port of Corpus Christi, TX Only has information on destination PWD and date and time in and out of the port area. No shifting details.

The Port of Coos Bay, OR Only has information on destination PWD and date and time in and out of the port area. No shifting details.

Patapsco River Ports including the port of Baltimore Harbor, MD


The Port of Tampa, FL

All PWDs or anchorages for shifting are named. Arrival and departure dates and times are noted for all movements, allowing calculation of maneuvering and hotelling both for individual shifts and the overall call.


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Table 3-12 Great Lake MEPA movements and shifts

MEPA Area and Ports MEPA Data Includes

Port of Cleveland, OH Information on the first and last PWD for the vessel (gives information for at most one shift per vessel). No information on intermediate PWDs..

Port of Burns Harbor, IN No shifting details, No PWDs listed..

Since 1999, several new detailed emissions inventories have been developed and were reviewed for use as additional or replacement typical ports: These included:

• Port of Los Angeles12,28

• Puget Sound Ports29

• Port of New York/New Jersey30

• Port of Houston/Galveston31

• Port of Beaumont/Port Arthur32

• Port of Corpus Christi33

• Port of Portland34

• Ports of Cleveland, OH and Duluth-Superior, MN&WI35

Based on the review of these newer studies, some of the previous typical ports were

replaced with newer data and an additional typical port was added. Data developed for Cleveland and Duluth-Superior for LADCO was used in lieu of the previous typical port data for Cleveland and Burns Harbor because it provided more detailed information and better engine category definitions. The Port of Houston/Galveston inventory provided enough data to add an additional typical port. All three port inventories were adjusted to reflect the current methodology used in this study.

The information provided in the current inventory for Puget Sound Ports29 was used to calculate RSZ speeds, load factors, and times for all Puget Sound ports. As described in Section 3.3.2.4.2, an additional modeled port was also added to account for the considerable amount of Jones Act tanker ship activity in the Puget Sound area that is not contained in the original inventory.

The newer Port of New York/New Jersey inventory provided a check against estimates made using the 1996 data. All other new inventory information was found to lack sufficient detail to prepare the detailed typical port inventories needed for this project.

The final list of nine deep sea and two Great Lake typical ports used in this analysis and

their data year is as follows:

• Lower Mississippi River Ports [1996]


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• Consolidated Ports of New York and New Jersey and Hudson River [1996]

• Delaware River Ports [1996]

• Puget Sound Area Ports [1996]

• Corpus Christi, TX [1996]

• Houston/Galveston Area Ports [1997]

• Ports on the Patapsco River [1996]

• Port of Coos Bay, OR [1996]

• Port of Tampa, FL [1996]

• Port of Cleveland, OH on Lake Erie [2005]

• Duluth-Superior, MN & WI on Lake Michigan [2005]

The maneuvering and hotelling time-in-modes, as well as the maneuvering load factors

for these typical ports, were binned by ship type, engine type, and DWT type, using the same bins described in Section 3.3.2.3.1.1.

3.3.2.3.8.2 Matching Typical Ports to Modeled Ports

The next step in the process was to match the ports to be modeled with the typical port which was most like it. Three criteria were used for matching a given port to a typical port: regional differences,B maximum vessel draft, and the ship types that call on a specific port. One container port, for instance, may have much smaller bulk cargo and reefer ships number of calls on that port than another. Using these three criteria and the eleven typical ports that are suitable for port matching, the 89 deep sea ports and 28 Great Lake ports were matched to the typical ports. For a typical port, the modeled and typical port is the same (i.e., the port simply represents itself). For California ports, we used data provided by ARB as discussed in Section 3.3.2.4. The matched ports for the deep sea ports are provided in Table 3-13.

Table 3-13 Matched Ports for the Deep Sea Ports Modeled Port Name Typical Like Port

Anacortes, WA Puget Sound

Barbers Point, HI Puget Sound

Everett, WA Puget Sound

Grays Harbor, WA Puget Sound

Honolulu, HI Puget Sound

Kalama, WA Puget Sound

Longview, WA Puget Sound

Olympia, WA Puget Sound

B The region in which a port was located was used to group top ports as it was considered a primary influence on the characteristics (size and installed power) of the vessels calling at those ports.


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Modeled Port Name Typical Like Port

Port Angeles, WA Puget Sound

Portland, OR Puget Sound

Seattle, WA Puget Sound

Tacoma, WA Puget Sound

Vancouver, WA Puget Sound

Valdez, AK Puget Sound

Other Puget Sound Puget Sound

Anchorage, AK Coos Bay

Coos Bay, OR Coos Bay

Hilo, HI Coos Bay

Kahului, HI Coos Bay

Nawiliwili, HI Coos Bay

Nikishka, AK Coos Bay

Beaumont, TX Houston

Freeport, TX Houston

Galveston, TX Houston

Houston, TX Houston

Port Arthur, TX Houston

Texas City, TX Houston

Corpus Christi, TX Corpus Christi

Lake Charles, LA Corpus Christi

Mobile, AL Corpus Christi

Brownsville, TX Tampa

Gulfport, MS Tampa

Manatee, FL Tampa

Matagorda Ship Tampa

Panama City, FL Tampa

Pascagoula, MS Tampa

Pensacola, FL Tampa

Tampa, FL Tampa

Everglades, FL Tampa

New Orleans, LA Lower Mississippi

Baton Rouge, LA Lower Mississippi

South Louisiana, LA Lower Mississippi

Plaquemines, LA Lower Mississippi

Albany, NY New York/New Jersey

New York/New Jersey New York/New Jersey

Portland, ME New York/New Jersey

Georgetown, SC Delaware River

Hopewell, VA Delaware River

Marcus Hook, PA Delaware River


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Modeled Port Name Typical Like Port

Morehead City, NC Delaware River

Paulsboro, NJ Delaware River

Chester, PA Delaware River

Fall River, MA Delaware River

New Castle, DE Delaware River

Penn Manor, PA Delaware River

Providence, RI Delaware River

Brunswick, GA Delaware River

Canaveral, FL Delaware River

Charleston, SC Delaware River

New Haven, CT Delaware River

Palm Beach, FL Delaware River

Bridgeport, CT Delaware River

Camden, NJ Delaware River

Philadelphia, PA Delaware River

Wilmington, DE Delaware River

Wilmington, NC Delaware River

Richmond, VA Delaware River

Jacksonville, FL Delaware River

Miami, FL Delaware River

Searsport, ME Delaware River

Boston, MA Delaware River

New Bedford/Fairhaven, MA Delaware River

Baltimore, MD Patapsco River

Newport News, VA Patapsco River

Savannah, GA Patapsco River

Catalina, CA ARB Supplied

Carquinez, CA ARB Supplied

El Segundo, CA ARB Supplied

Eureka, CA ARB Supplied

Hueneme, CA ARB Supplied

Long Beach, CA ARB Supplied

Los Angeles, CA ARB Supplied

Oakland, CA ARB Supplied

Redwood City, CA ARB Supplied

Richmond, CA ARB Supplied

Sacramento, CA ARB Supplied

San Diego, CA ARB Supplied

San Francisco, CA ARB Supplied

Stockton, CA ARB Supplied


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Great Lake ports were matched to either Cleveland or Duluth as shown in Table 3-14.

Table 3-14 Great Lake Match Ports

Port Name Typical Like Port Alpena, MI Cleveland

Buffalo, NY Cleveland

Burns Waterway, IN Cleveland

Calcite, MI Cleveland

Cleveland, OH Cleveland

Dolomite, MI Cleveland

Erie, PA Cleveland

Escanaba, MI Cleveland

Fairport, OH Cleveland

Gary, IN Cleveland

Lorain, OH Cleveland

Marblehead, OH Cleveland

Milwaukee, WI Cleveland

Muskegon, MI Cleveland

Presque Isle, MI Cleveland

St Clair, MI Cleveland

Stoneport, MI Cleveland

Two Harbors, MN Cleveland

Ashtabula, OH Duluth-Superior

Chicago, IL Duluth-Superior

Conneaut, OH Duluth-Superior

Detroit, MI Duluth-Superior

Duluth-Superior, MN&WI Duluth-Superior

Indiana, IN Duluth-Superior

Inland Harbor, MI Duluth-Superior

Manistee, MI Duluth-Superior

Sandusky, OH Duluth-Superior

Toledo, OH Duluth-Superior

Once a modeled port was matched to a typical port, the maneuvering and hotelling time-in-mode values, as well as the maneuvering load factors by bin for the typical ports, were used directly for the modeled ports, with no adjustments. The other inputs used for both the typical and modeled ports are as described in Section 3.3.2.3.


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3.3.2.3.8.3 Bin Mismatches

In some cases, the specific DWT range bin at the modeled port was not in the typical like port data. In those cases, the next nearest DWT range bin was used for the calculations. In a few cases, the engine type for a given ship type might not be in the typical like port data. In these cases, the closest engine type at the typical like port was used. Also in a few cases, a specific ship type in the modeled port data was not in the typical like port data. In this case, the nearest like ship type at the typical port was chosen to calculate emissions at the modeled port.

3.3.2.4 Stand Alone Ports

In a few cases, the USACE entrances and clearances data was not used to calculate emissions at the modeled port. These include the California ports for which we received data from ARB, the Port of Valdez, Alaska, and a conglomerate port within the Puget Sound area, as described below.

3.3.2.4.1 California Ports

The California Air Resources Board (ARB) supplied inventories for 14 California ports for 2002. The data received from ARB for the California ports were modified to provide consistent PM and SO2 emissions to those calculated in this report. In addition, cruise and RSZ emissions were calculated directly based upon average ship power provided in the ARB methodology document36 and number of calls, because ARB did not calculate cruise emissions, and transit (RSZ) emissions were allocated to counties instead of ports. ARB provided transit distances for each port to calculate the RSZ emissions. Ship propulsion and auxiliary engine power were calculated based upon the methodology in Section 3.3.2.3.1.3 for use in computing cruise and RSZ emissions. For maneuvering and hotelling emissions, the ARB values were used and adjusted as discussed below. The data supplied by ARB included domestic traffic as well as foreign cargo traffic.

For PM emission calculations, ARB used an emission factor of 1.5 g/kWh to calculate total PM emissions and factors of 0.96 and 0.937 to convert total PM to PM10 and PM2.5 respectively. Since an emission factor of 1.4 g/kWh was used in our calculations for PM10 and an emission factor of 1.3 g/kWh for PM2.5, ARB PM10 and PM2.5 emissions were multiplied by factors of 0.972 and 0.925, respectively to get consistent PM10 and PM2.5 emissions for propulsion engines.

For auxiliary engines, ARB used the same emission factors as above, while we used PM10 and PM2.5 emission factors of 1.3 and 1.2 g/kWh, respectively for passenger ships and 1.1 and 1.0 g/kWh, respectively for all other ships. In the ARB inventory, all passenger ships are treated as electric drive and all emissions are allocated to auxiliary engines. ARB auxiliary engine emissions were thus multiplied by factors of 0.903 and 0.854 respectively for passenger ships and 0.764 and 0.711 respectively for other ships to provide consistent PM emission calculations.


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SO2 emissions were also different between the ARB and these analyses. ARB used a compositeC propulsion engine SO2 emission factor of 10.55 g/kWh while we used a composite SO2 emission factor of 9.57 g/kWh. Thus, ARB SO2 propulsion emissions were multiplied by a factor of 0.907 to be consistent with our emission calculations. For auxiliary engines, ARB used SO2 emission factors of 11.48 and 9.34 g/kWh, respectively for passenger and other ships, while we use emission factors of 9.93 and 9.07 g/kWh, respectively. Thus, ARB auxiliary SO2 emissions were multiplied by factors of 0.865 and 0.971, respectively for passenger and other ships to provide consistent SO2 emissions.

3.3.2.4.2 Port in Puget Sound

In the newest Puget Sound inventory29, it was found that a considerable amount of tanker ships stop at Cherry Point, Ferndale, March Point and other areas which are not within the top 89 U.S. deep sea ports analyzed in this analysis. In addition, since they are ships carrying U.S. cargo (oil from Alaska) from one U.S. port to another, they are not documented in the USACE entrances and clearances data. To compensate for this anomaly, an additional port was added which encompassed these tanker ships stopping within the Puget Sound area but not at one of the Puget Sound ports analyzed in this analysis. Ship calls in the 1996 typical port data to ports other than those in the top 89 U.S. deep sea ports were analyzed separately. There were 363 ship calls by tankers to those areas in 1996. In the inventory report for 2005, there were 468 calls. For 2002, it was estimated there were 432 calls. The same ship types and ship characteristics were used as in the 1996 data, but the number of calls was proportionally increased to 432 calls to represent these ships. The location of the “Other Puget Sound” port was approximately at Cherry Point near Aberdeen.

3.3.2.4.3 Port of Valdez

In a recent Alaska port inventory,37 it was found that significant Category 3 domestic tanker traffic enters and leaves the Port of Valdez on destination to West Coast ports. Since the USACE entrances and clearances data did not contain any tanker calls at Valdez in 2002, the recent Alaska inventory data was used to calculate emissions at that port. In this case, the number of calls and ship characteristics for 2002 were taken directly from the Alaska inventory and used in determining emissions for the modeled port with the Puget Sound area typical port being used as the like port.

3.3.2.5 Domestic Traffic

One of the concerns with using USACE entrances and clearances data is that it only contains foreign cargo movements moved by either a foreign flag vessel or a U.S. flag vessel. The Maritime Administration (MARAD) maintains the Foreign Traffic Vessel Entrances and Clearances database, which contains statistics on U.S. foreign maritime trade. Data are compiled during the regular processing of statistics on foreign imports and exports. The database contains information on the type of vessel, commodities, weight, customs districts and ports, and origins and destinations of goods. Thus domestic traffic, i.e., U.S. ships delivering cargo from one U.S. C Based upon ARB assuming 95 percent of the engines were SSD and 5 percent were MSD. The composite SO2 EF

of 9.57 g/kW-hr was calculated using this weighting, along with the SSD and MSD SO2 EFs for the West Coast ports reported in Table 3-6.


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port to another U.S. port, is covered under the Jones Act and is not accounted for in the database. However, U.S. flagged ships carrying cargo from a foreign port to a U.S. port or from a U.S. port to a foreign port are accounted for in the USACE entrances and clearances database, as these are considered foreign cargo movements.

Under the Jones Act, domestic cargo movements from one U.S. port to another U.S. port must be carried by a U.S. flag ship. The Jones Act also requires ships traveling between United States ports to be constructed by United States companies and owned by a United States company or citizen. Members of the ships’ crews must be United States citizens or legal aliens. Because of the use of USACE data, in the present baseline and future year inventories, only limited Jones Act ships were counted. These ships included those servicing California ports, those serving the Port of Valdez and those serving other Puget Sound ports. At all other ports, Jones Act ships were not counted.

ICF conducted an analysis to estimate the amount of Category 3 Jones Act ships calling at the 117 U.S. ports. This was done by analyzing marine exchange data obtained from port authorities for eleven typical ports and using this information to estimate the Jones Act ship contribution for the remaining ports. Based on this limited analysis, Jones Act ships are estimated to account for 9.2% of the total installed power calling on U.S. ports. Approximately 30% of these ships, largely in the Alaska and Pacific regions, have been included in the 2002 baseline inventory. Based on this analysis, Jones Act ships excluded from this inventory constitute roughly 6.5% of total installed power.38 This results in an underestimation of the port ship inventory and therefore the benefits of the coordinated program reported in this chapter are also underestimated.

3.3.2.6 2002 Near Port Inventories

This section presents a summary of the baseline near port inventories for 2002. Individual port inventories are presented separately for deep sea ports and Great Lake ports because of the difference in ship types between the two. This is followed by totals for the summed port inventories, provided by engine type (propulsion and auxiliary), mode of operation, and ship type.

3.3.2.6.1 Deep Sea Ports

Emission inventories for the 89 deep sea ports are presented here. Total emissions (propulsion and auxiliary) by ports are given in Table 3-15. Auxiliary only emissions by ports are given in Table 3-16. Emissions by mode are given in Table 3-17 for cruise, Table 3-18 for reduced speed zone, Table 3-19 for maneuvering, and Table 3-20 for hotelling. Emissions by ship type by port are given in Table 3-21 through Table 3-31. Ports that are missing from those lists had no emissions related to that ship type during 2002.

For deep sea ports, auxiliary emissions are responsible for roughly 47% of the NOX and PM emissions, primarily due to emissions during the hotelling mode. Container and Tanker ships combined are responsible for approximately half the total emissions, followed by Passenger ships and Bulk Carrier ships.


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3.3.2.6.2 Great Lake Ports

Emissions inventories for 28 Great Lake ports were developed and are presented here. Great Lake ships include self-unloading bulk carriers (Bulk Carrier, SU) which tend to operate within the Great Lakes only. Other ships travel down the St. Lawrence River from the open ocean. Integrated tug-barges (ITB) are also used on the Great Lakes.

Total emissions by port for Great Lakes Ports are shown in Table 3-32. Auxiliary engine emissions for Great Lake ports are shown in Table 3-33. Emissions by mode for Great Lake ports are shown in Table 3-34 for cruise, Table 3-35 for reduced speed zone, Table 3-36 for maneuvering, and Table 3-37 for hotelling. Emissions by ship type are shown in Table 3-38 through Table 3-42.


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Table 3-15 Total Emissions by Deep Sea Port in 2002 Metric Tonnes

Port Name

Installed Power (MW) NOX PM10 PM2.5 HC CO SO2 CO2

Anacortes, WA 545 403 32 29 14 32 225 15,462 Barbers Point, HI 472 122 10 9 4 10 71 5,034 Everett, WA 186 82 7 6 3 7 46 3,125 Grays Harbor, WA 360 50 4 4 2 4 30 2,066 Honolulu, HI 8,037 1,268 116 102 47 102 800 54,385 Kalama, WA 1,190 359 30 26 13 30 210 14,555 Longview, WA 1,619 413 34 30 15 35 239 16,495 Olympia, WA 97 56 4 4 2 4 31 2,047 Port Angeles, WA 556 151 13 11 5 12 89 6,042 Portland, OR 11,198 2,307 206 182 117 223 1,320 90,558 Seattle, WA 26,292 6,669 573 513 265 551 3,789 253,190 Tacoma, WA 19,130 5,742 477 428 217 464 3,211 215,754 Vancouver, WA 1,946 446 37 33 17 39 259 17,821 Valdez, AK 6,676 343 37 33 11 27 299 20,789 Other Puget Sound 5,678 2,111 219 197 71 169 1,745 118,629 Anchorage, AK 537 221 18 16 7 17 133 8,236 Coos Bay, OR 399 46 4 3 2 4 27 1,810 Hilo, HI 4,514 929 77 70 27 72 626 44,368 Kahului, HI 2,323 474 39 35 14 37 312 22,094 Nawiliwili, HI 591 122 10 9 4 9 83 5,884 Nikishka, AK 1,110 270 26 24 8 21 209 13,794 Beaumont, TX 12,699 2,106 261 240 91 189 1,972 83,736 Freeport, TX 7,411 714 92 85 25 54 716 28,422 Galveston, TX 6,572 1,014 118 102 35 69 873 43,643 Houston, TX 47,147 4,625 546 491 158 347 4,136 183,952 Port Arthur, TX 3,531 436 52 47 17 37 388 17,342 Texas City, TX 7,382 970 127 117 33 74 986 38,575 Corpus Christi, TX 11,452 1,758 143 132 59 401 1,090 70,240 Lake Charles, LA 6,382 850 80 74 35 239 594 38,409 Mobile, AL 8,200 1,144 95 88 39 303 724 46,155 Brownsville, TX 1,213 175 14 13 6 14 108 7,057 Gulfport, MS 3,556 607 51 46 20 48 414 26,382 Manatee, FL 2,903 667 56 49 22 53 450 28,904 Matagorda Ship 2,504 389 32 28 14 33 239 15,827 Panama City, FL 662 70 6 5 2 6 44 2,789 Pascagoula, MS 3,566 518 44 40 17 42 344 22,223 Pensacola, FL 351 40 3 3 1 3 27 1,726 Tampa, FL 10,941 1,507 129 109 50 121 988 63,033 Everglades, FL 38,304 4,287 402 372 134 334 3,123 198,127 New Orleans, LA 27,575 6,603 556 513 221 536 4,245 272,794 Baton Rouge, LA 4,627 1,985 160 148 63 155 1,223 78,568 South Louisiana, LA 18,366 6,428 519 479 203 502 3,976 257,346 Plaquemines, LA 4,230 1,045 85 78 33 82 658 43,258 Albany, NY 396 103 9 8 4 9 65 4,167 New York/New Jersey 86,980 7,364 622 575 274 621 4,620 296,780 Portland, ME 3,968 722 60 55 23 57 466 30,836 Georgetown, SC 609 89 7 7 3 7 152 3,668 Hopewell, VA 185 45 4 3 2 4 211 1,764


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Table 3-15 Total Emissions by Deep Sea Port in 2002 (continued) Metric Tonnes

Port Name


Marcus Hook, PA 2,754 965 79 73 30 76 2,462 40,563 Morehead City, NC 967 121 10 9 4 10 94 5,196 Paulsboro, NJ 3,272 668 55 50 22 54 2,103 26,676 Chester, PA 1,467 196 16 15 7 16 411 7,648 Fall River, MA 290 35 3 3 1 3 52 1,748 New Castle, DE 765 199 16 15 6 16 394 8,257 Penn Manor, PA 721 174 14 13 6 14 656 6,878 Providence, RI 1,097 198 16 15 6 16 334 8,222 Brunswick, GA 5,184 670 54 50 22 53 1,297 26,273 Canaveral, FL 17,801 3,060 281 261 89 233 2,279 139,768 Charleston, SC 46,233 3,809 311 288 133 310 4,519 150,424 New Haven, CT 1,801 287 23 22 9 22 207 12,116 Palm Beach, FL 2,277 219 19 18 7 17 162 9,869 Bridgeport, CT 1,452 247 20 19 8 19 164 10,692 Camden, NJ 4,209 994 82 76 34 83 1,625 41,540 Philadelphia, PA 7,644 1,684 140 129 55 140 3,363 70,523 Wilmington, DE 4,444 627 52 48 23 54 1,011 25,319 Wilmington, NC 4,888 641 53 49 22 52 956 26,264 Richmond, VA 596 86 7 7 3 8 206 3,333 Jacksonville, FL 13,908 1,507 125 116 51 122 1,652 62,457 Miami, FL 57,415 7,155 650 602 218 551 5,340 322,880 Searsport, ME 543 110 9 8 3 9 124 4,769 Boston, MA 13,290 1,647 146 135 53 131 1,572 74,625 New Bedford/Fairhaven, MA 181 39 3 3 1 3 33 1,700 Baltimore, MD 25,197 6,412 519 481 212 502 3,918 244,560 Newport News, VA 5,529 505 41 38 17 41 316 19,760 Savannah, GA 37,523 3,594 289 267 126 291 2,174 137,046 Catalina, CA 928 78 7 7 2 6 53 3,639 Carquinez, CA 3,442 537 39 36 17 42 309 20,535 El Segundo, CA 1,685 192 14 13 6 15 108 7,095 Eureka, CA 409 82 6 5 2 6 51 3,486 Hueneme, CA 3,334 319 22 21 10 280 190 12,820 Long Beach, CA 56,935 5,303 389 357 166 417 3,141 213,005 Los Angeles, CA 50,489 4,793 352 324 150 378 2,839 192,430 Oakland, CA 48,762 3,022 222 205 100 239 1,638 110,003 Redwood City, CA 456 107 8 7 3 8 64 4,317 Richmond, CA 3,956 484 35 33 15 37 277 18,361 Sacramento, CA 455 138 10 9 4 11 81 5,417 San Diego, CA 8,255 840 68 63 25 65 536 36,609 San Francisco, CA 6,260 684 53 49 21 53 419 28,356 Stockton, CA 1,210 332 24 22 10 26 192 12,830 Total Port Emissions 863,191 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 Total Port Emissions (short tons) 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005


3-41

Table 3-16 Auxiliary Engine Emissions by Deep Sea Port in 2002 Metric Tonnes

Port Name


Anacortes, WA 115 147 11 10 4 11 92 6,798 Barbers Point, HI 101 77 6 5 2 6 48 3,568 Everett, WA 40 21 2 1 1 2 13 977 Grays Harbor, WA 73 25 2 2 1 2 16 1,176 Honolulu, HI 2,043 793 67 61 22 60 522 36,366 Kalama, WA 260 172 13 12 5 13 108 7,930 Longview, WA 346 183 14 13 5 14 115 8,445 Olympia, WA 21 9 1 1 0 1 6 410 Port Angeles, WA 111 42 3 3 1 3 26 1,922 Portland, OR 2,560 924 70 64 26 70 580 42,675 Seattle, WA 5,947 1,472 116 106 41 112 939 67,795 Tacoma, WA 4,305 1,279 97 88 35 97 802 59,093 Vancouver, WA 427 182 14 13 5 14 114 8,402 Valdez, AK 1,411 256 20 18 7 19 161 11,836 Other Puget Sound 1,198 951 72 66 26 72 596 43,927 Anchorage, AK 158 99 8 7 3 8 63 3,683 Coos Bay, OR 78 21 2 2 1 2 14 949 Hilo, HI 1,251 815 64 58 23 64 529 38,048 Kahului, HI 642 412 32 29 12 32 267 19,178 Nawiliwili, HI 164 108 8 8 3 8 70 5,023 Nikishka, AK 235 132 10 9 4 10 83 5,623 Beaumont, TX 2,415 873 149 135 31 63 1,188 40,334 Freeport, TX 1,342 321 58 53 11 24 461 14,819 Galveston, TX 1,645 674 89 75 24 42 660 31,135 Houston, TX 8,410 1,827 305 268 64 129 2,352 84,373 Port Arthur, TX 640 173 29 25 6 12 220 8,002 Texas City, TX 1,414 418 78 71 15 31 626 19,301 Corpus Christi, TX 2,486 770 64 59 21 59 514 35,563 Lake Charles, LA 1,347 457 38 35 13 35 305 21,105 Mobile, AL 1,816 423 35 32 12 32 282 19,529 Brownsville, TX 260 84 7 6 2 6 56 3,899 Gulfport, MS 878 415 34 30 11 31 292 19,017 Manatee, FL 902 491 41 35 13 37 343 22,448 Matagorda Ship 535 202 17 14 6 15 131 9,318 Panama City, FL 130 28 2 2 1 2 19 1,315 Pascagoula, MS 795 277 23 20 8 21 187 12,772 Pensacola, FL 87 20 2 1 1 1 14 906 Tampa, FL 2,639 777 67 51 21 59 534 35,735 Everglades, FL 9,813 3,032 277 256 84 230 2,158 140,039 New Orleans, LA 6,376 3,426 295 271 95 260 2,343 158,234 Baton Rouge, LA 988 813 67 62 22 62 543 37,544 South Louisiana, LA 3,988 2,969 246 226 82 226 1,982 137,151 Plaquemines, LA 919 607 50 46 17 46 406 28,058 Albany, NY 85 46 4 3 1 3 31 2,111 New York/New Jersey 20,036 3,467 294 270 96 263 2,343 159,839 Portland, ME 883 477 40 37 13 36 320 22,034 Georgetown, SC 129 42 3 3 1 3 28 1,960 Hopewell, VA 40 16 1 1 0 1 11 757


3-42

Table 3-16 Auxiliary Engine Emissions by Deep Sea Port in 2002 (continued) Metric Tonnes

Port Name


Marcus Hook, PA 583 617 51 47 17 47 412 28,518 Morehead City, NC 203 74 6 6 2 6 49 3,421 Paulsboro, NJ 701 294 25 23 8 22 198 13,584 Chester, PA 318 63 5 5 2 5 42 2,897 Fall River, MA 61 17 2 2 1 2 15 1,035 New Castle, DE 164 120 10 9 3 9 80 5,532 Penn Manor, PA 159 69 6 5 2 5 46 3,204 Providence, RI 236 118 10 9 3 9 79 5,436 Brunswick, GA 1,302 263 22 20 7 20 176 12,160 Canaveral, FL 4,916 2,486 225 209 68 187 1,804 113,582 Charleston, SC 10,277 1,630 136 124 45 124 1,093 75,271 New Haven, CT 379 188 16 14 5 14 125 8,664 Palm Beach, FL 506 132 11 10 4 10 89 6,082 Bridgeport, CT 522 187 15 14 5 14 125 8,625 Camden, NJ 1,286 579 48 44 16 44 387 26,754 Philadelphia, PA 1,803 976 81 74 27 74 652 45,081 Wilmington, DE 1,155 303 25 23 8 23 202 13,982 Wilmington, NC 1,045 333 28 25 9 25 223 15,397 Richmond, VA 130 26 2 2 1 2 18 1,216 Jacksonville, FL 3,242 776 64 59 21 59 516 35,693 Miami, FL 14,504 5,171 462 428 142 389 3,711 236,659 Searsport, ME 116 73 6 6 2 6 49 3,380 Boston, MA 3,100 1,105 94 87 30 84 759 50,846 New Bedford/Fairhaven, MA 53 28 2 2 1 2 19 1,280 Baltimore, MD 5,924 1,632 137 126 45 52 1,111 75,309 Newport News, VA 1,216 170 14 13 5 13 122 8,063 Savannah, GA 8,297 1,035 83 76 29 79 691 47,804 Catalina, CA 257 45 4 4 1 3 28 2,043 Carquinez, CA 772 193 13 11 5 15 128 8,706 El Segundo, CA 355 47 3 3 1 4 32 2,117 Eureka, CA 88 59 4 4 2 5 38 2,661 Hueneme, CA 1,010 177 11 10 5 47 115 7,955 Long Beach, CA 13,007 2,632 178 162 72 205 1,704 119,333 Los Angeles, CA 11,535 2,356 160 145 65 184 1,525 106,855 Oakland, CA 10,759 860 57 52 24 67 551 39,102 Redwood City, CA 101 59 4 3 2 5 39 2,665 Richmond, CA 866 164 11 10 5 13 109 7,403 Sacramento, CA 95 61 4 4 2 5 40 2,754 San Diego, CA 2,164 483 37 34 13 37 311 21,942 San Francisco, CA 1,480 345 25 23 9 27 224 15,630 Stockton, CA 259 125 8 7 3 10 82 5,673 Total Auxiliary Emissions 197,430 57,317 5,052 4,597 1,615 4,306 41,232 2,635,436 Total Auxiliary Emissions (short tons) 63,181 5,569 5,067 1,781 4,746 45,450 2,905,071


3-43

Table 3-17 Cruise Emissions by Deep Sea Port in 2002 Metric Tonnes

Port Name


Anacortes, WA 545 50 4 4 2 4 29 1,871 Barbers Point, HI 472 28 2 2 1 2 16 1,039 Everett, WA 186 9 1 1 0 1 6 385 Grays Harbor, WA 360 15 1 1 1 1 10 627 Honolulu, HI 8,037 300 28 26 10 23 206 13,469 Kalama, WA 1,190 72 6 6 2 6 45 2,949 Longview, WA 1,619 89 8 7 3 7 55 3,597 Olympia, WA 97 5 0 0 0 0 3 184 Port Angeles, WA 556 27 2 2 1 2 17 1,134 Portland, OR 11,198 424 40 37 15 33 291 19,040 Seattle, WA 26,292 775 74 69 27 59 544 35,599 Tacoma, WA 19,130 622 59 55 22 49 428 28,010 Vancouver, WA 1,946 88 8 7 3 7 56 3,650 Valdez, AK 6,676 45 8 8 2 4 75 4,904 Other Puget Sound 5,678 197 24 22 7 15 202 13,218 Anchorage, AK 537 22 2 2 1 2 14 934 Coos Bay, OR 399 21 2 2 1 2 12 758 Hilo, HI 4,514 108 14 13 4 9 109 7,278 Kahului, HI 2,323 58 7 6 2 5 51 3,382 Nawiliwili, HI 591 14 2 2 1 1 15 984 Nikishka, AK 1,110 32 4 4 1 3 34 2,220 Beaumont, TX 12,699 665 52 48 22 51 384 23,253 Freeport, TX 7,411 362 28 26 12 28 209 12,624 Galveston, TX 6,572 283 23 22 9 22 175 10,741 Houston, TX 47,147 2,180 173 161 72 169 1,290 78,115 Port Arthur, TX 3,531 184 15 13 6 14 108 6,521 Texas City, TX 7,382 386 30 28 13 30 224 13,579 Corpus Christi, TX 11,452 584 46 43 19 45 341 20,702 Lake Charles, LA 6,382 266 25 23 9 21 195 11,811 Mobile, AL 8,200 402 33 30 13 31 247 14,961 Brownsville, TX 1,213 69 5 5 2 5 40 2,453 Gulfport, MS 3,556 148 13 12 5 12 95 5,765 Manatee, FL 2,903 132 11 10 4 10 82 4,991 Matagorda Ship 2,504 143 11 10 5 11 83 5,021 Panama City, FL 662 35 3 3 1 3 20 1,240 Pascagoula, MS 3,566 181 15 14 6 15 118 7,155 Pensacola, FL 351 16 1 1 1 1 10 635 Tampa, FL 10,941 539 45 42 18 42 341 20,705 Everglades, FL 38,304 1,348 131 121 45 104 1,038 62,951 New Orleans, LA 27,575 1,249 102 94 41 97 761 46,164 Baton Rouge, LA 4,627 238 19 17 8 18 139 8,439 South Louisiana, LA 18,366 961 75 70 32 74 557 33,789 Plaquemines, LA 4,230 221 17 16 7 17 128 7,766 Albany, NY 396 20 2 2 1 2 12 734 New York/New Jersey 86,980 3,266 261 242 108 253 1,940 117,641 Portland, ME 3,968 195 16 15 6 15 118 7,131 Georgetown, SC 609 31 3 2 1 2 19 1,153 Hopewell, VA 185 10 1 1 0 1 6 356


3-44

Table 3-17 Cruise Emissions by Deep Sea Port in 2002 (continued)

Metric Tonnes

Port Name


Marcus Hook, PA 2,754 143 11 10 5 11 82 4,974 Morehead City, NC 967 44 4 3 1 3 28 1,687 Paulsboro, NJ 3,272 166 13 12 5 13 97 5,887 Chester, PA 1,467 63 5 5 2 5 37 2,261 Fall River, MA 290 13 1 1 0 1 9 540 New Castle, DE 765 41 3 3 1 3 23 1,415 Penn Manor, PA 721 38 3 3 1 3 22 1,351 Providence, RI 1,097 58 4 4 2 4 33 2,007 Brunswick, GA 5,184 222 17 16 7 17 129 7,816 Canaveral, FL 17,801 665 54 50 22 52 501 30,423 Charleston, SC 46,233 1,702 133 123 56 132 986 59,738 New Haven, CT 1,801 92 7 7 3 7 54 3,259 Palm Beach, FL 2,277 83 8 7 3 6 60 3,623 Bridgeport, CT 1,452 58 4 4 2 5 34 2,073 Camden, NJ 4,209 191 15 14 6 15 113 6,874 Philadelphia, PA 7,644 326 26 24 11 25 194 11,761 Wilmington, DE 4,444 178 14 13 6 14 104 6,283 Wilmington, NC 4,888 213 17 16 7 16 125 7,597 Richmond, VA 596 25 2 2 1 2 15 891 Jacksonville, FL 13,908 571 46 43 19 44 349 21,139 Miami, FL 57,415 2,068 173 161 70 161 1,497 90,831 Searsport, ME 543 27 2 2 1 2 17 1,018 Boston, MA 13,290 465 41 38 16 36 340 20,603 New Bedford/Fairhaven, MA 181 8 1 1 0 1 5 331 Baltimore, MD 25,197 1,013 81 75 34 78 600 36,410 Newport News, VA 5,529 214 17 16 7 17 125 7,560 Savannah, GA 37,523 1,400 110 102 46 108 815 49,371 Catalina, CA 928 36 4 3 1 3 26 1,700 Carquinez, CA 3,442 171 13 12 6 13 92 6,025 El Segundo, CA 1,685 87 7 6 3 7 47 3,068 Eureka, CA 409 19 2 1 1 1 11 699 Hueneme, CA 3,334 137 11 10 5 11 74 4,862 Long Beach, CA 56,935 2,093 168 156 69 162 1,165 76,254 Los Angeles, CA 50,489 1,856 149 138 62 144 1,033 67,622 Oakland, CA 48,762 1,676 131 122 55 130 900 58,866 Redwood City, CA 456 24 2 2 1 2 13 851 Richmond, CA 3,956 197 15 14 7 15 106 6,936 Sacramento, CA 455 23 2 2 1 2 13 821 San Diego, CA 8,255 336 30 28 11 26 217 14,243 San Francisco, CA 6,260 273 23 21 9 21 162 10,632 Stockton, CA 1,210 63 5 5 2 5 34 2,216 Total Cruise Emissions 863,191 34,193 2,826 2,623 1,141 2,651 21,186 1,314,146 Total Cruise Emissions (short tons) 37,691 3,115 2,891 1,258 2,922 23,353 1,448,598


3-45

Table 3-18 Reduced Speed Zone Emissions by Deep Sea Port in 2002 Metric Tonnes

Port Name


Anacortes, WA 545 191 15 14 6 15 103 6,773 Barbers Point, HI 472 3 0 0 0 0 2 125 Everett, WA 186 49 4 4 2 4 27 1,785 Grays Harbor, WA 360 3 0 0 0 0 2 109 Honolulu, HI 8,037 75 7 6 3 6 48 3,223 Kalama, WA 1,190 101 8 7 4 9 57 3,800 Longview, WA 1,619 125 10 9 5 11 70 4,645 Olympia, WA 97 43 3 3 1 3 23 1,509 Port Angeles, WA 556 77 6 6 3 6 45 2,924 Portland, OR 11,198 969 86 79 58 108 539 36,288 Seattle, WA 26,292 4,289 349 323 151 347 2,402 157,988 Tacoma, WA 19,130 3,685 290 269 121 285 2,023 133,271 Vancouver, WA 1,946 175 14 13 7 16 100 6,661 Valdez, AK 6,676 33 5 5 1 3 46 3,044 Other Puget Sound 5,678 963 112 104 32 75 942 61,929 Anchorage, AK 537 121 10 9 4 10 71 3,721 Coos Bay, OR 399 5 0 0 0 1 3 123 Hilo, HI 4,514 27 2 2 1 2 18 339 Kahului, HI 2,323 14 1 1 0 1 9 156 Nawiliwili, HI 591 4 0 0 0 0 2 47 Nikishka, AK 1,110 117 12 12 4 9 99 5,979 Beaumont, TX 12,699 771 81 75 45 88 574 29,868 Freeport, TX 7,411 28 2 2 1 2 18 1,016 Galveston, TX 6,572 101 10 9 4 8 73 3,958 Houston, TX 47,147 656 57 53 22 50 429 24,233 Port Arthur, TX 3,531 97 10 9 6 11 71 3,760 Texas City, TX 7,382 181 16 14 6 14 117 6,581 Corpus Christi, TX 11,452 419 33 31 14 293 250 15,432 Lake Charles, LA 6,382 175 20 19 13 185 124 7,805 Mobile, AL 8,200 352 29 27 12 239 219 13,537 Brownsville, TX 1,213 23 2 1 1 2 12 879 Gulfport, MS 3,556 50 4 3 2 5 27 2,070 Manatee, FL 2,903 78 7 4 3 7 36 3,183 Matagorda Ship 2,504 55 5 3 3 6 27 2,117 Panama City, FL 662 7 1 0 0 1 4 263 Pascagoula, MS 3,566 68 6 5 2 6 40 2,788 Pensacola, FL 351 5 0 0 0 0 3 225 Tampa, FL 10,941 329 29 16 12 28 159 13,321 Everglades, FL 38,304 71 7 7 3 7 52 3,225 New Orleans, LA 27,575 2,670 224 208 98 227 1,678 103,988 Baton Rouge, LA 4,627 1,091 87 80 36 85 648 40,082 South Louisiana, LA 18,366 2,897 229 212 95 225 1,712 105,846 Plaquemines, LA 4,230 244 19 18 8 19 144 8,910 Albany, NY 396 48 4 4 2 5 30 1,845 New York/New Jersey 86,980 881 83 76 54 105 547 34,706 Portland, ME 3,968 48 4 4 2 4 30 1,839 Georgetown, SC 609 16 1 1 1 1 105 615 Hopewell, VA 185 22 2 2 1 2 196 781


3-46

Table 3-18 Reduced Speed Zone Emissions by Deep Sea Port in 2002(continued) Metric Tonnes

Port Name


Marcus Hook, PA 2,754 245 20 18 9 20 1,996 9,058 Morehead City, NC 967 2 0 0 0 0 16 75 Paulsboro, NJ 3,272 254 21 19 9 21 1,841 9,527 Chester, PA 1,467 86 7 7 3 8 343 3,292 Fall River, MA 290 5 0 0 0 0 29 231 New Castle, DE 765 45 4 3 1 4 295 1,671 Penn Manor, PA 721 82 7 6 3 7 598 3,045 Providence, RI 1,097 26 2 2 1 2 225 971 Brunswick, GA 5,184 215 17 16 7 17 1,015 7,867 Canaveral, FL 17,801 73 7 7 2 6 94 3,316 Charleston, SC 46,233 539 44 41 22 50 2,504 20,265 New Haven, CT 1,801 4 0 0 0 0 27 146 Palm Beach, FL 2,277 5 0 0 0 0 14 235 Bridgeport, CT 1,452 2 0 0 0 0 6 98 Camden, NJ 4,209 346 29 27 14 32 1,208 13,693 Philadelphia, PA 7,644 505 43 40 19 48 2,603 19,709 Wilmington, DE 4,444 206 17 16 10 20 747 7,996 Wilmington, NC 4,888 110 9 9 5 10 620 4,169 Richmond, VA 596 44 4 3 2 4 180 1,688 Jacksonville, FL 13,908 206 17 16 8 19 820 8,030 Miami, FL 57,415 182 17 16 6 15 331 8,194 Searsport, ME 543 11 1 1 0 1 59 442 Boston, MA 13,290 135 13 12 6 13 514 6,009 New Bedford/Fairhaven, MA 181 4 0 0 0 0 10 158 Baltimore, MD 25,197 4,325 347 321 142 336 2,596 159,626 Newport News, VA 5,529 131 11 10 5 11 86 4,998 Savannah, GA 37,523 1,333 107 99 46 110 802 49,492 Catalina, CA 928 11 1 1 0 1 8 523 Carquinez, CA 3,442 183 14 13 6 14 100 6,591 El Segundo, CA 1,685 58 5 4 2 5 32 2,093 Eureka, CA 409 4 0 0 0 0 2 165 Hueneme, CA 3,334 8 1 1 0 256 5 251 Long Beach, CA 56,935 748 62 58 30 69 436 29,056 Los Angeles, CA 50,489 755 63 58 30 69 440 29,305 Oakland, CA 48,762 524 43 40 23 53 272 18,380 Redwood City, CA 456 25 2 2 1 2 14 905 Richmond, CA 3,956 123 10 9 4 10 67 4,427 Sacramento, CA 455 58 5 4 2 4 32 2,088 San Diego, CA 8,255 98 9 8 3 8 63 4,198 San Francisco, CA 6,260 101 8 8 3 8 61 4,015 Stockton, CA 1,210 156 12 11 5 12 85 5,586 Total RSZ Emissions 863,191 34,427 2,887 2,657 1,280 3,804 35,148 1,318,897 Total RSZ Emissions (short tons) 37,949 3,182 2,929 1,410 4,193 38,744 1,453,835


3-47

Table 3-19 Maneuvering Emissions by Deep Sea Port in 2002 Metric Tonnes

Port Name


Anacortes, WA 545 50 5 3 3 5 23 1,610 Barbers Point, HI 472 25 2 2 1 2 12 806 Everett, WA 186 9 1 1 1 1 4 301 Grays Harbor, WA 360 12 1 1 1 1 6 412 Honolulu, HI 8,037 360 36 28 19 32 194 13,248 Kalama, WA 1,190 63 6 4 4 6 31 2,122 Longview, WA 1,619 72 7 5 4 7 35 2,411 Olympia, WA 97 3 0 0 0 0 2 109 Port Angeles, WA 556 19 2 1 1 2 10 666 Portland, OR 11,198 501 49 37 33 50 232 16,173 Seattle, WA 26,292 980 100 76 70 98 445 30,829 Tacoma, WA 19,130 810 81 62 57 82 368 25,644 Vancouver, WA 1,946 75 7 5 4 8 37 2,538 Valdez, AK 6,676 55 8 6 3 5 46 3,156 Other Puget Sound 5,678 252 29 22 13 25 163 11,182 Anchorage, AK 537 1 0 0 0 0 1 54 Coos Bay, OR 399 1 0 0 0 0 0 26 Hilo, HI 4,514 12 1 1 1 1 8 557 Kahului, HI 2,323 6 1 1 0 1 4 283 Nawiliwili, HI 591 1 0 0 0 0 1 73 Nikishka, AK 1,110 2 0 0 0 0 1 90 Beaumont, TX 12,699 49 14 12 2 4 95 1,909 Freeport, TX 7,411 23 7 6 1 2 45 898 Galveston, TX 6,572 40 12 5 1 3 38 1,676 Houston, TX 47,147 169 47 31 6 13 255 6,754 Port Arthur, TX 3,531 17 5 3 1 1 25 683 Texas City, TX 7,382 28 8 7 1 2 59 1,063 Corpus Christi, TX 11,452 112 11 10 8 14 68 4,385 Lake Charles, LA 6,382 54 6 5 4 6 38 2,414 Mobile, AL 8,200 70 7 6 5 8 44 2,835 Brownsville, TX 1,213 8 1 1 1 1 7 323 Gulfport, MS 3,556 27 3 2 2 3 20 1,025 Manatee, FL 2,903 33 3 3 2 4 25 1,301 Matagorda Ship 2,504 16 2 1 1 2 13 609 Panama City, FL 662 4 0 0 0 0 3 144 Pascagoula, MS 3,566 20 2 2 1 2 18 829 Pensacola, FL 351 2 0 0 0 0 2 68 Tampa, FL 10,941 66 7 6 4 8 95 2,637 Everglades, FL 38,304 233 24 23 12 23 163 10,273 New Orleans, LA 27,575 192 19 17 13 22 118 7,540 Baton Rouge, LA 4,627 35 3 3 2 4 21 1,371 South Louisiana, LA 18,366 143 14 12 10 18 87 5,606 Plaquemines, LA 4,230 33 3 3 2 4 20 1,297 Albany, NY 396 3 0 0 0 0 2 120 New York/New Jersey 86,980 455 46 42 36 54 265 17,069 Portland, ME 3,968 37 4 3 2 4 23 1,472 Georgetown, SC 609 3 0 0 0 0 2 126 Hopewell, VA 185 1 0 0 0 0 1 39


3-48

Table 3-19 Maneuvering Emissions by Deep Sea Port in 2002 (continued) Metric Tonnes

Port Name


Marcus Hook, PA 2,754 22 2 2 2 3 14 874 Morehead City, NC 967 5 0 0 0 1 3 204 Paulsboro, NJ 3,272 24 2 2 2 3 15 953 Chester, PA 1,467 5 1 1 0 1 3 204 Fall River, MA 290 1 0 0 0 0 1 60 New Castle, DE 765 5 0 0 0 1 3 196 Penn Manor, PA 721 4 0 0 0 0 2 159 Providence, RI 1,097 7 1 1 0 1 4 269 Brunswick, GA 5,184 25 2 2 2 3 15 974 Canaveral, FL 17,801 70 7 6 3 6 50 3,118 Charleston, SC 46,233 199 20 19 17 24 112 7,263 New Haven, CT 1,801 11 1 1 1 1 7 435 Palm Beach, FL 2,277 9 1 1 1 1 6 388 Bridgeport, CT 1,452 10 1 1 1 1 6 419 Camden, NJ 4,209 27 3 2 2 3 17 1,090 Philadelphia, PA 7,644 46 4 4 3 6 28 1,790 Wilmington, DE 4,444 22 2 2 2 3 13 861 Wilmington, NC 4,888 24 2 2 2 3 14 922 Richmond, VA 596 2 0 0 0 0 1 79 Jacksonville, FL 13,908 66 6 6 5 8 40 2,587 Miami, FL 57,415 241 25 23 14 24 164 10,379 Searsport, ME 543 4 0 0 0 0 2 147 Boston, MA 13,290 65 7 6 4 7 44 2,812 New Bedford/Fairhaven, MA 181 1 0 0 0 0 1 52 Baltimore, MD 25,197 130 13 12 10 15 76 4,931 Newport News, VA 5,529 25 3 2 2 3 14 929 Savannah, GA 37,523 164 17 15 14 20 91 5,936 Catalina, CA 928 10 1 1 0 1 6 455 Carquinez, CA 3,442 23 1 1 1 1 11 740 El Segundo, CA 1,685 9 1 1 0 1 4 287 Eureka, CA 409 4 0 0 0 0 2 133 Hueneme, CA 3,334 9 0 0 0 1 4 294 Long Beach, CA 56,935 272 15 13 6 15 120 8,669 Los Angeles, CA 50,489 242 13 12 5 13 106 7,687 Oakland, CA 48,762 241 10 9 5 11 89 6,472 Redwood City, CA 456 3 0 0 0 0 1 83 Richmond, CA 3,956 26 2 1 1 2 12 838 Sacramento, CA 455 3 0 0 0 0 1 84 San Diego, CA 8,255 80 6 6 2 6 46 3,409 San Francisco, CA 6,260 54 4 4 1 4 29 2,105 Stockton, CA 1,210 7 0 0 0 0 3 220 Total Maneuver Emissions 863,191 7,383 758 625 440 724 4,356 266,262 Total Maneuver Emissions (short tons) 8,138 835 689 485 799 4,802 293,504


3-49

Table 3-20 Hotelling Emissions by Deep Sea Port in 2002 Metric Tonnes

Port Name


Anacortes, WA 545 113 9 8 3 9 71 5,207 Barbers Point, HI 472 66 5 5 2 5 42 3,064 Everett, WA 186 14 1 1 0 1 9 653 Grays Harbor, WA 360 20 2 1 1 2 12 918 Honolulu, HI 8,037 533 45 41 15 40 352 24,445 Kalama, WA 1,190 123 9 9 3 9 77 5,684 Longview, WA 1,619 126 10 9 3 10 79 5,842 Olympia, WA 97 5 0 0 0 0 3 245 Port Angeles, WA 556 29 2 2 1 2 18 1,319 Portland, OR 11,198 413 31 29 11 31 259 19,057 Seattle, WA 26,292 625 49 45 17 47 399 28,774 Tacoma, WA 19,130 624 47 43 17 47 391 28,829 Vancouver, WA 1,946 108 8 7 3 8 67 4,972 Valdez, AK 6,676 210 16 15 6 16 132 9,685 Other Puget Sound 5,678 699 53 48 19 53 438 32,299 Anchorage, AK 537 76 6 5 2 6 47 3,527 Coos Bay, OR 399 20 1 1 1 1 12 903 Hilo, HI 4,514 784 60 54 22 60 491 36,194 Kahului, HI 2,323 396 30 27 11 30 248 18,273 Nawiliwili, HI 591 103 8 7 3 8 65 4,780 Nikishka, AK 1,110 119 9 8 3 9 75 5,505 Beaumont, TX 12,699 622 114 105 22 46 919 28,707 Freeport, TX 7,411 301 55 51 11 22 445 13,884 Galveston, TX 6,572 590 73 67 21 36 587 27,267 Houston, TX 47,147 1,621 269 246 57 115 2,162 74,850 Port Arthur, TX 3,531 138 23 21 5 10 184 6,379 Texas City, TX 7,382 376 73 67 13 28 585 17,352 Corpus Christi, TX 11,452 643 53 49 18 49 430 29,720 Lake Charles, LA 6,382 355 29 27 10 27 237 16,379 Mobile, AL 8,200 321 27 24 9 24 214 14,822 Brownsville, TX 1,213 74 6 6 2 6 49 3,402 Gulfport, MS 3,556 382 31 29 10 29 272 17,521 Manatee, FL 2,903 425 35 32 12 32 307 19,428 Matagorda Ship 2,504 175 15 13 5 13 117 8,080 Panama City, FL 662 25 2 2 1 2 16 1,141 Pascagoula, MS 3,566 248 21 19 7 19 168 11,451 Pensacola, FL 351 17 1 1 0 1 12 797 Tampa, FL 10,941 573 49 45 16 43 392 26,370 Everglades, FL 38,304 2,634 240 222 73 200 1,870 121,678 New Orleans, LA 27,575 2,492 211 194 69 189 1,688 115,102 Baton Rouge, LA 4,627 621 51 47 17 47 414 28,676 South Louisiana, LA 18,366 2,427 201 185 67 185 1,620 112,104 Plaquemines, LA 4,230 547 45 42 15 42 365 25,286 Albany, NY 396 32 3 2 1 2 21 1,467 New York/New Jersey 86,980 2,762 234 215 76 210 1,867 127,364 Portland, ME 3,968 442 37 34 12 34 296 20,394 Georgetown, SC 609 38 3 3 1 3 26 1,773 Hopewell, VA 185 13 1 1 0 1 9 589


3-50

Table 3-20 Hotelling Emissions by Deep Sea Port in 2002 (continued)

Metric Tonnes

Port Name


Marcus Hook, PA 2,754 555 46 42 15 42 371 25,657 Morehead City, NC 967 70 6 5 2 5 47 3,230 Paulsboro, NJ 3,272 223 19 17 6 17 150 10,309 Chester, PA 1,467 41 3 3 1 3 27 1,891 Fall River, MA 290 15 2 2 1 2 13 918 New Castle, DE 765 108 9 8 3 8 72 4,975 Penn Manor, PA 721 50 4 4 1 4 34 2,323 Providence, RI 1,097 108 9 8 3 8 72 4,975 Brunswick, GA 5,184 208 17 16 6 16 139 9,616 Canaveral, FL 17,801 2,252 213 198 62 169 1,634 102,912 Charleston, SC 46,233 1,368 114 105 38 104 917 63,159 New Haven, CT 1,801 179 15 14 5 14 120 8,276 Palm Beach, FL 2,277 122 10 9 3 9 82 5,623 Bridgeport, CT 1,452 175 15 13 5 13 117 8,102 Camden, NJ 4,209 430 36 33 12 33 287 19,882 Philadelphia, PA 7,644 807 67 61 22 61 539 37,262 Wilmington, DE 4,444 220 18 17 6 17 147 10,180 Wilmington, NC 4,888 294 24 22 8 22 196 13,576 Richmond, VA 596 15 1 1 0 1 10 675 Jacksonville, FL 13,908 665 55 51 18 51 444 30,702 Miami, FL 57,415 4,665 434 402 128 351 3,348 213,476 Searsport, ME 543 68 6 5 2 5 46 3,163 Boston, MA 13,290 982 85 78 27 74 673 45,202 New Bedford/Fairhaven, MA 181 25 2 2 1 2 17 1,160 Baltimore, MD 25,197 944 79 73 26 72 646 43,593 Newport News, VA 5,529 136 11 10 4 10 91 6,274 Savannah, GA 37,523 698 55 50 19 53 466 32,248 Catalina, CA 928 21 2 2 1 2 13 961 Carquinez, CA 3,442 159 10 9 4 13 107 7,178 El Segundo, CA 1,685 37 2 2 1 3 25 1,646 Eureka, CA 409 55 4 3 2 4 36 2,489 Hueneme, CA 3,334 164 11 10 5 13 107 7,413 Long Beach, CA 56,935 2,189 144 130 60 172 1,420 99,027 Los Angeles, CA 50,489 1,941 127 116 53 152 1,259 87,816 Oakland, CA 48,762 581 37 34 16 46 376 26,285 Redwood City, CA 456 55 4 3 2 4 36 2,479 Richmond, CA 3,956 137 9 8 4 11 92 6,160 Sacramento, CA 455 54 3 3 1 4 35 2,424 San Diego, CA 8,255 326 23 21 9 25 209 14,758 San Francisco, CA 6,260 257 18 16 7 20 167 11,604 Stockton, CA 1,210 107 7 6 3 8 70 4,808 Total Hotel Emissions 863,191 45,603 4,060 3,726 1,287 3,456 33,218 2,096,566 Total Hotel Emissions (short tons) 50,268 4,475 4,107 1,419 3,809 36,617 2,311,068


3-51

Table 3-21 Auto Carrier Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Honolulu, HI 539 59 5 5 3 5 35 2,397 Port Angeles, WA 6 1 0 0 0 0 1 47 Portland, OR 2,331 416 38 33 21 42 246 16,911 Seattle, WA 9 3 0 0 0 0 2 109 Tacoma, WA 2,123 733 61 55 27 59 414 27,690 Vancouver, WA 278 48 4 4 2 5 28 1,946 Beaumont, TX 31 4 1 1 0 0 4 195 Galveston, TX 560 59 6 5 2 4 43 2,372 Houston, TX 1,141 122 12 11 4 8 92 5,019 Mobile, AL 692 72 6 6 2 16 47 2,993 Manatee, FL 4 0 0 0 0 0 0 14 Matagorda Ship 16 1 0 0 0 0 1 48 Pensacola, FL 169 13 1 1 0 1 8 520 Tampa, FL 284 24 2 2 1 2 15 994 Everglades, FL 136 22 2 2 1 2 14 938 New Orleans, LA 225 50 4 4 2 4 32 2,089 South Louisiana, LA 16 3 0 0 0 0 2 129 New York/New Jersey 4,588 361 30 28 15 32 218 13,923 Morehead City, NC 35 3 0 0 0 0 2 102 Chester, PA 9 2 0 0 0 0 4 65 Brunswick, GA 3,350 368 30 28 12 29 499 14,351 Canaveral, FL 53 4 0 0 0 0 3 153 Charleston, SC 1,922 182 15 14 6 14 169 7,234 Bridgeport, CT 40 3 0 0 0 0 2 133 Camden, NJ 0 0 0 0 0 0 0 0 Philadelphia, PA 111 16 1 1 1 1 27 604 Wilmington, DE 1,012 126 10 10 5 11 180 5,014 Jacksonville, FL 4,420 389 32 29 14 32 362 15,430 Miami, FL 131 10 1 1 0 1 7 395 Boston, MA 744 62 5 5 2 5 54 2,495 Baltimore, MD 5,458 1,290 103 95 43 101 768 48,152 Newport News, VA 270 27 2 2 1 2 20 1,127 Savannah, GA 644 76 6 6 3 6 46 2,898 Carquinez, CA 682 84 6 6 3 6 49 3,246 Hueneme, CA 2,036 125 9 8 4 157 71 4,650 Long Beach, CA 1,068 96 7 6 3 7 55 3,681 Los Angeles, CA 947 87 6 6 3 7 50 3,339 Oakland, CA 10 1 0 0 0 0 1 42 Richmond, CA 468 51 4 3 2 4 30 1,986 San Diego, CA 1,374 131 9 9 4 10 77 5,123 San Francisco, CA 20 2 0 0 0 0 1 81 Total Auto Carrier 37,954 5,125 421 384 185 577 3,676 198,637 Total Auto Carrier (short tons) 5,649 464 424 204 636 4,052 218,960


3-52

Table 3-22 Barge Carrier Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Mobile, AL 2 0 0 0 0 0 0 17 New Orleans, LA 472 87 8 7 3 8 57 3,738 Morehead City, NC 73 6 1 1 0 0 5 330 Charleston, SC 420 55 4 4 2 4 78 2,279 Total Barge Carrier 967 148 13 12 5 12 141 6,364 Total Barge Carrier (short tons) 163 14 13 6 14 156 7,015


3-53

Table 3-23 Bulk Carrier Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Anacortes, WA 67 28 2 2 1 2 15 1,033 Barbers Point, HI 82 14 1 1 1 1 9 599 Everett, WA 71 33 3 2 1 3 18 1,206 Grays Harbor, WA 140 24 2 2 1 2 14 974 Honolulu, HI 158 29 2 2 1 2 17 1,188 Kalama, WA 1,007 233 19 17 8 20 136 9,408 Longview, WA 1,142 265 22 19 10 22 154 10,659 Olympia, WA 73 45 4 3 2 4 24 1,628 Port Angeles, WA 72 22 2 2 1 2 12 848 Portland, OR 2,351 633 51 46 23 53 364 25,061 Seattle, WA 523 244 19 18 8 19 135 9,103 Tacoma, WA 872 445 35 32 15 35 247 16,617 Vancouver, WA 1,003 256 21 19 9 22 147 10,127 Valdez, AK 7 1 0 0 0 0 1 59 Anchorage, AK 52 22 2 2 1 2 12 763 Coos Bay, OR 87 10 1 1 0 1 6 389 Hilo, HI 31 3 0 0 0 0 2 125 Kahului, HI 34 4 0 0 0 0 2 145 Nikishka, AK 246 74 6 5 2 6 41 2,609 Beaumont, TX 1,055 185 19 16 9 18 129 6,998 Freeport, TX 392 35 4 3 1 3 25 1,347 Galveston, TX 1,063 114 11 9 4 8 78 4,285 Houston, TX 5,996 655 66 54 22 48 446 24,640 Port Arthur, TX 890 106 11 9 4 9 74 4,025 Texas City, TX 481 60 6 5 2 4 40 2,221 Corpus Christi, TX 3,359 460 37 34 16 121 278 17,665 Lake Charles, LA 1,116 147 13 12 6 46 91 5,870 Mobile, AL 2,752 401 32 30 14 115 241 15,258 Brownsville, TX 685 106 9 8 3 8 65 4,234 Gulfport, MS 120 21 2 2 1 2 13 834 Manatee, FL 322 60 5 4 2 5 36 2,364 Matagorda Ship 586 118 10 9 4 10 71 4,713 Panama City, FL 79 13 1 1 0 1 8 515 Pascagoula, MS 586 116 9 8 4 9 70 4,586 Pensacola, FL 25 4 0 0 0 0 3 178 Tampa, FL 3,380 604 49 43 20 48 365 23,968 Everglades, FL 626 109 9 8 3 9 70 4,652 New Orleans, LA 8,311 2,511 202 187 79 196 1,550 100,577


3-54

Table 3-23 Bulk Carrier Deep Sea Port Emissions in 2002 (continued) Metric Tonnes

Port Name


Baton Rouge, LA 1,668 722 58 53 23 56 439 28,070 South Louisiana, LA 11,606 4,014 323 298 127 313 2,470 159,561 Plaquemines, LA 2,714 665 54 50 21 52 417 27,385 Albany, NY 280 79 6 6 3 7 49 3,152 New York/New Jersey 3,168 482 41 37 16 39 317 20,791 Portland, ME 470 62 5 5 2 5 38 2,458 Georgetown, SC 408 63 5 5 2 5 116 2,606 Hopewell, VA 127 30 2 2 1 2 144 1,167 Marcus Hook, PA 243 54 4 4 2 4 192 2,146 Morehead City, NC 130 17 1 1 1 1 13 692 Paulsboro, NJ 168 38 3 3 1 3 57 1,522 Chester, PA 35 7 1 1 0 1 26 289 Fall River, MA 127 13 2 1 1 1 30 792 New Castle, DE 240 51 4 4 2 4 37 2,080 Penn Manor, PA 659 161 13 12 5 13 637 6,326 Providence, RI 511 78 6 6 3 6 154 3,157 Brunswick, GA 370 75 6 6 2 6 276 2,934 Canaveral, FL 464 59 5 4 2 5 54 2,453 Charleston, SC 1,589 238 19 18 8 19 449 9,729 New Haven, CT 424 55 4 4 2 4 43 2,282 Palm Beach, FL 83 11 1 1 0 1 9 442 Bridgeport, CT 98 13 1 1 0 1 10 547 Camden, NJ 775 176 14 13 6 14 714 6,918 Philadelphia, PA 473 105 8 8 3 8 296 4,161 Wilmington, DE 345 66 5 5 2 5 215 2,611 Wilmington, NC 422 68 5 5 2 5 160 2,718 Richmond, VA 11 3 0 0 0 0 18 117 Jacksonville, FL 1,394 203 17 15 6 16 337 8,436 Miami, FL 122 16 1 1 1 1 17 653 Searsport, ME 37 6 0 0 0 0 9 227 Boston, MA 450 59 5 5 2 5 90 2,652 Baltimore, MD 2,851 1,273 102 95 41 99 773 48,126 Newport News, VA 692 118 10 9 4 9 78 4,815 Savannah, GA 1,474 334 27 25 11 26 205 13,237 Carquinez, CA 717 172 12 11 5 13 103 6,934 Eureka, CA 114 28 2 2 1 2 18 1,201 Long Beach, CA 2,297 468 33 30 14 36 283 19,185 Los Angeles, CA 2,037 423 29 27 13 33 255 17,295 Oakland, CA 280 40 3 3 1 3 23 1,568 Redwood City, CA 437 103 7 7 3 8 61 4,155 Richmond, CA 385 82 6 5 2 6 50 3,371 Sacramento, CA 218 72 5 5 2 6 42 2,842 San Diego, CA 350 64 4 4 2 5 39 2,638 San Francisco, CA 498 101 7 6 3 8 61 4,139 Stockton, CA 638 198 14 13 6 15 116 7,780 Total Bulk Carrier 82,437 19,373 1,570 1,431 633 1,732 14,945 767,825 Total Bulk Carrier (short tons) 21,355 1,731 1,577 697 1,909 16,474 846,382


3-55

Table 3-24 Container Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Everett, WA 24 6 0 0 0 0 3 210 Honolulu, HI 2,190 308 30 25 15 27 181 12,403 Port Angeles, WA 14 2 0 0 0 0 1 78 Portland, OR 5,227 879 85 74 59 96 486 33,142 Seattle, WA 21,749 5,230 445 396 218 441 2,857 191,094 Tacoma, WA 15,446 3,109 264 236 124 253 1,741 116,552 Vancouver, WA 7 4 0 0 0 0 2 143 Freeport, TX 1,575 74 6 6 2 6 46 2,679 Galveston, TX 427 22 2 2 1 2 14 792 Houston, TX 13,441 698 59 55 23 53 446 25,617 Corpus Christi, TX 24 2 0 0 0 0 1 84 Lake Charles, LA 36 4 0 0 0 1 2 135 Mobile, AL 39 4 0 0 0 1 2 155 Gulfport, MS 1,538 181 15 14 7 15 110 7,411 Everglades, FL 7,732 658 56 52 23 53 426 27,826 New Orleans, LA 5,756 788 65 60 35 76 482 30,940 South Louisiana, LA 36 5 0 0 0 1 3 197 Plaquemines, LA 12 1 0 0 0 0 1 41 New York/New Jersey 56,253 3,246 268 248 130 281 1,934 122,010 Morehead City, NC 24 2 0 0 0 0 1 58 Chester, PA 1,140 139 11 10 5 11 306 5,313 Charleston, SC 37,982 2,691 219 202 97 222 3,001 103,968 New Haven, CT 14 1 0 0 0 0 1 34 Palm Beach, FL 752 44 4 4 2 3 32 1,861 Philadelphia, PA 2,696 306 25 23 13 28 671 11,715 Wilmington, DE 1,999 197 16 15 8 18 379 7,555 Wilmington, NC 1,779 130 11 10 5 12 162 5,115 Richmond, VA 539 74 6 6 3 7 182 2,807 Jacksonville, FL 3,997 279 24 22 11 24 279 11,419 Miami, FL 20,834 1,310 107 99 46 105 961 51,282 Boston, MA 5,016 325 27 25 13 28 305 12,667 Baltimore, MD 9,224 1,411 112 104 49 113 828 51,462 Newport News, VA 3,797 251 20 19 9 20 148 9,311 Savannah, GA 28,209 2,088 168 156 77 173 1,230 76,805 Carquinez, CA 27 3 0 0 0 0 2 105 Eureka, CA 55 6 0 0 0 0 4 245 Hueneme, CA 82 6 0 0 0 6 4 250 Long Beach, CA 42,292 3,434 244 225 109 272 1,986 134,894 Los Angeles, CA 37,505 3,097 221 203 99 247 1,791 121,601 Oakland, CA 47,109 2,833 208 192 94 224 1,532 102,880 Richmond, CA 165 15 1 1 0 1 8 571 San Diego, CA 385 30 2 2 1 2 17 1,168 San Francisco, CA 1,209 102 7 7 3 8 59 4,003 Total Container Ship 378,355 33,990 2,733 2,494 1,282 2,833 22,628 1,288,596 Total Container Ship (short tons) 37,468 3,012 2,749 1,413 3,123 24,944 1,420,434


3-56

Table 3-25 General Cargo Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Anacortes, WA 23 5 0 0 0 0 3 218 Everett, WA 58 19 2 1 1 1 11 764 Grays Harbor, WA 220 26 2 2 1 2 16 1,093 Honolulu, HI 43 6 1 1 0 1 4 294 Kalama, WA 116 15 1 1 1 1 8 568 Longview, WA 441 61 5 5 2 5 35 2,376 Olympia, WA 24 11 1 1 0 1 6 419 Port Angeles, WA 390 90 7 7 3 7 49 3,291 Portland, OR 771 123 11 9 5 11 71 4,812 Seattle, WA 841 261 21 19 9 21 145 9,653 Tacoma, WA 264 105 9 8 4 8 61 4,096 Vancouver, WA 514 73 6 6 3 7 43 2,924 Valdez, AK 6 1 0 0 0 0 1 39 Anchorage, AK 4 1 0 0 0 0 1 48 Coos Bay, OR 312 36 3 3 1 3 21 1,421 Hilo, HI 5 1 0 0 0 0 0 21 Kahului, HI 7 1 0 0 0 0 0 29 Nikishka, AK 24 7 1 0 0 1 4 247 Beaumont, TX 744 113 12 12 5 11 89 4,691 Freeport, TX 238 22 2 2 1 2 16 845 Galveston, TX 111 12 1 1 0 1 9 486 Houston, TX 5,806 560 59 52 19 42 439 23,458 Port Arthur, TX 890 100 11 9 4 9 77 4,085 Texas City, TX 46 6 1 1 0 0 4 232 Corpus Christi, TX 188 20 2 2 1 5 14 876 Lake Charles, LA 670 71 7 6 3 22 49 3,150 Mobile, AL 2,529 297 25 23 10 85 190 11,928 Brownsville, TX 206 23 2 2 1 2 15 949 Gulfport, MS 496 51 4 4 2 4 32 2,048 Manatee, FL 301 36 3 3 1 3 22 1,430 Matagorda Ship 27 2 0 0 0 0 2 104 Panama City, FL 545 52 4 4 2 4 33 2,070 Pascagoula, MS 466 45 4 4 2 4 30 1,915 Pensacola, FL 71 7 1 1 0 1 5 287 Tampa, FL 986 118 10 9 4 9 75 4,784 Everglades, FL 1,813 197 18 16 6 16 138 9,057 New Orleans, LA 2,925 601 50 46 20 48 384 24,538 Baton Rouge, LA 356 111 10 9 4 9 73 4,624 South Louisiana, LA 810 216 18 16 7 17 134 8,502 Plaquemines, LA 178 29 2 2 1 2 19 1,192 Albany, NY 83 15 1 1 1 1 10 639 New York/New Jersey 1,841 153 13 12 6 13 95 5,957 Georgetown, SC 202 26 2 2 1 2 35 1,062 Hopewell, VA 44 12 1 1 0 1 42 444 Marcus Hook, PA 39 7 1 1 0 1 16 299 Morehead City, NC 387 40 3 3 1 3 30 1,684 Paulsboro, NJ 22 3 0 0 0 0 2 145 Chester, PA 237 40 3 3 1 3 71 1,679


3-57

Table 3-25 General Cargo Ship Deep Sea Port Emissions in 2002 (continued) Metric Tonnes

Port Name


Fall River, MA 139 17 2 1 1 1 16 774 Penn Manor, PA 56 12 1 1 0 1 18 500 Providence, RI 32 4 0 0 0 0 7 158 Brunswick, GA 1,066 168 14 12 6 14 475 6,535 Canaveral, FL 549 61 5 5 2 5 52 2,509 Charleston, SC 1,814 223 18 17 7 18 343 9,045 New Haven, CT 382 43 4 3 1 3 30 1,791 Palm Beach, FL 722 76 7 6 2 6 54 3,524 Camden, NJ 974 180 15 14 6 15 349 7,471 Philadelphia, PA 960 164 14 13 6 14 315 6,907 Wilmington, DE 185 28 2 2 1 2 43 1,165 Wilmington, NC 1,178 155 13 12 5 12 237 6,288 Richmond, VA 38 7 1 1 0 1 5 322 Jacksonville, FL 1,419 152 13 12 5 12 160 6,422 Miami, FL 2,941 354 31 29 11 28 272 16,024 Searsport, ME 3 0 0 0 0 0 1 17 Boston, MA 122 14 1 1 0 1 13 606 Baltimore, MD 2,275 673 56 52 22 52 430 26,796 Newport News, VA 568 74 6 6 2 6 47 3,033 Savannah, GA 2,521 415 34 32 14 32 261 16,543 Carquinez, CA 39 8 1 1 0 1 5 331 Eureka, CA 183 42 3 3 1 3 26 1,750 Hueneme, CA 77 7 0 0 0 10 4 262 Long Beach, CA 996 158 11 10 5 12 94 6,364 Los Angeles, CA 883 143 10 9 4 11 85 5,742 Oakland, CA 462 43 3 3 1 3 23 1,579 Redwood City, CA 19 4 0 0 0 0 2 163 Richmond, CA 67 13 1 1 0 1 8 530 Sacramento, CA 202 58 4 4 2 5 34 2,292 San Diego, CA 867 144 10 9 4 11 87 5,901 San Francisco, CA 453 82 6 5 2 6 50 3,375 Stockton, CA 202 55 4 4 2 4 32 2,147 Total General Cargo 49,711 7,402 630 576 251 684 6,208 302,338 Total General Cargo (short tons) 8,159 694 635 277 754 6,843 333,270


3-58

Table 3-26 Miscellaneous Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Honolulu, HI 16 4 0 0 0 0 2 149 Portland, OR 21 7 1 0 0 0 4 269 Seattle, WA 9 5 0 0 0 0 3 180 Anchorage, AK 58 22 2 2 1 2 15 992 Kahului, HI 1 0 0 0 0 0 0 11 Houston, TX 13 1 0 0 0 0 1 49 Corpus Christi, TX 119 16 2 1 1 5 12 759 Lake Charles, LA 3 0 0 0 0 0 0 18 Mobile, AL 604 83 8 7 3 24 62 3,903 Pensacola, FL 65 11 1 1 0 1 8 497 New Orleans, LA 12 7 1 1 0 1 4 281 New York/New Jersey 26 7 1 1 0 1 5 325 Baltimore, MD 23 14 1 1 0 1 10 674 Newport News, VA 6 2 0 0 0 0 2 103 Total Miscellaneous 976 179 16 15 6 35 128 8,209 Total Miscellaneous (short tons) 197 18 17 7 39 141 9,049


3-59

Table 3-27 Passenger Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Honolulu, HI 4,359 637 58 53 19 48 427 28,546 Portland, OR 60 12 1 1 1 1 8 558 Seattle, WA 3,017 739 72 66 23 54 540 35,669 Valdez, AK 31 2 0 0 0 0 2 110 Anchorage, AK 200 66 5 5 2 5 43 2,495 Hilo, HI 4,467 923 76 70 27 72 622 44,123 Kahului, HI 2,256 466 38 35 14 36 307 21,755 Nawiliwili, HI 583 120 10 9 4 9 82 5,810 Galveston, TX 3,248 644 76 64 23 42 559 28,782 Houston, TX 751 143 19 15 5 9 131 6,539 Corpus Christi, TX 113 21 2 2 1 3 14 954 Mobile, AL 330 80 7 6 2 11 52 3,538 Manatee, FL 634 66 7 5 2 5 52 3,064 Tampa, FL 3,599 352 34 25 12 28 271 16,166 Everglades, FL 22,083 2,447 244 227 73 187 1,897 117,326 New Orleans, LA 5,401 1,133 110 102 37 91 835 51,550 New York/New Jersey 6,841 745 74 68 25 59 551 34,382 Portland, ME 380 31 3 3 1 2 25 1,523 Paulsboro, NJ 126 30 3 3 1 2 23 1,395 Fall River, MA 11 1 0 0 0 0 1 63 Canaveral, FL 15,756 2,758 256 238 80 209 2,044 126,856 Charleston, SC 758 101 9 9 3 8 75 4,652 Palm Beach, FL 146 15 1 1 0 1 11 684 Philadelphia, PA 44 11 1 1 0 1 8 508 Miami, FL 28,808 4,919 463 430 142 373 3,712 230,290 Boston, MA 2,878 431 41 38 13 33 327 20,219 New Bedford/Fairhaven, MA 16 2 0 0 0 0 2 94 Baltimore, MD 1,058 427 42 39 13 33 320 19,829 Savannah, GA 16 5 1 0 0 0 4 243 Catalina, CA 919 78 7 7 2 6 53 3,608 Eureka, CA 57 6 1 1 0 0 4 290 Hueneme, CA 29 2 0 0 0 4 1 100 Long Beach, CA 5,756 567 52 48 17 43 382 26,353 Los Angeles, CA 5,105 516 47 43 16 40 348 23,970 San Diego, CA 5,172 456 42 38 14 35 307 21,178 San Francisco, CA 2,241 214 19 18 7 16 144 9,935 Total Passenger 127,251 19,165 1,819 1,668 578 1,470 14,184 893,157 Total Passenger (short tons) 21,126 2,005 1,838 638 1,620 15,635 984,538


3-60

Table 3-28 Refrigerated Cargo Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Honolulu, HI 6 3 0 0 0 0 2 113 Port Angeles, WA 3 1 0 0 0 0 1 57 Seattle, WA 55 30 2 2 1 2 17 1,203 Anchorage, AK 140 62 5 4 2 5 36 2,256 Galveston, TX 532 87 9 8 3 6 70 3,724 Houston, TX 78 13 1 1 0 1 11 563 Corpus Christi, TX 97 21 2 2 1 3 13 897 Mobile, AL 22 5 0 0 0 1 3 209 Gulfport, MS 374 56 5 4 2 4 37 2,320 Manatee, FL 1,277 453 37 33 14 36 307 19,845 Pascagoula, MS 232 54 5 4 2 4 38 2,387 Pensacola, FL 2 0 0 0 0 0 0 13 Tampa, FL 245 38 3 3 1 3 25 1,599 Everglades, FL 116 71 6 5 2 5 47 3,223 New Orleans, LA 163 109 9 8 3 9 72 4,907 New York/New Jersey 1,575 195 16 15 7 16 123 8,151 Morehead City, NC 6 1 0 0 0 0 1 56 Paulsboro, NJ 4 1 0 0 0 0 1 53 Brunswick, GA 158 32 3 2 1 2 20 1,373 Canaveral, FL 525 96 8 7 3 7 63 4,212 Charleston, SC 82 16 1 1 0 1 10 684 Bridgeport, CT 1,086 188 15 14 6 15 121 8,196 Camden, NJ 2,088 531 44 41 19 45 341 22,716 Philadelphia, PA 833 206 17 16 7 18 132 8,874 Wilmington, DE 733 171 14 13 6 14 110 7,319 Jacksonville, FL 173 34 3 3 1 3 22 1,483 Miami, FL 742 130 11 10 4 10 84 5,666 Searsport, ME 5 1 0 0 0 0 1 44 New Bedford/Fairhaven, MA 69 15 1 1 0 1 10 682 Baltimore, MD 45 58 5 4 2 4 38 2,641 Hueneme, CA 963 161 11 10 5 81 99 6,839 Long Beach, CA 662 94 6 6 3 7 56 3,884 Los Angeles, CA 587 84 6 5 2 7 51 3,494 San Diego, CA 48 9 1 1 0 1 5 378 Total Reefer 13,724 3,027 247 226 98 313 1,968 130,060 Total Reefer (short tons) 3,337 273 249 108 345 2,170 143,367


3-61

Table 3-29 Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Barbers Point, HI 4 0 0 0 0 0 0 3 Everett, WA 27 2 0 0 0 0 1 64 Honolulu, HI 39 3 0 0 0 0 2 129 Longview, WA 5 1 0 0 0 0 1 39 Portland, OR 110 8 1 1 0 1 5 325 Seattle, WA 11 4 0 0 0 0 2 150 Tacoma, WA 148 45 4 3 2 4 25 1,654 Vancouver, WA 11 2 0 0 0 0 1 81 Anchorage, AK 5 2 0 0 0 0 1 75 Beaumont, TX 62 13 1 1 1 1 9 521 Galveston, TX 59 7 1 1 0 0 5 290 Houston, TX 810 99 10 9 3 7 75 4,089 Corpus Christi, TX 6 1 0 0 0 0 1 33 Lake Charles, LA 6 1 0 0 0 0 1 33 Mobile, AL 69 11 1 1 0 2 7 454 Gulfport, MS 1,028 299 26 23 9 23 222 13,769 Manatee, FL 3 1 0 0 0 0 1 36 Pensacola, FL 18 5 0 0 0 0 4 231 Tampa, FL 166 48 4 4 1 4 36 2,230 Everglades, FL 3,734 285 27 25 10 23 209 13,387 New Orleans, LA 783 132 11 10 5 12 81 5,237 South Louisiana, LA 4 1 0 0 0 0 1 34 Albany, NY 6 1 0 0 0 0 1 50 New York/New Jersey 3,323 290 24 22 11 25 176 11,292 Portland, ME 305 28 3 2 1 2 20 1,316 Morehead City, NC 27 2 0 0 0 0 2 104 Chester, PA 47 8 1 1 0 1 5 302 Penn Manor, PA 6 1 0 0 0 0 1 53 Brunswick, GA 219 23 2 2 1 2 14 880 Canaveral, FL 75 9 1 1 0 1 6 418 Charleston, SC 455 43 4 3 1 3 28 1,777 New Haven, CT 32 3 0 0 0 0 2 163 Palm Beach, FL 423 49 4 4 2 4 35 2,302 Bridgeport, CT 23 3 0 0 0 0 2 110 Camden, NJ 22 4 0 0 0 0 3 178 Philadelphia, PA 175 31 3 3 1 3 22 1,456 Wilmington, DE 10 1 0 0 0 0 1 48 Wilmington, NC 342 35 3 3 1 3 22 1,390 Richmond, VA 8 2 0 0 0 0 1 87 Jacksonville, FL 855 102 9 8 3 8 71 4,617 Miami, FL 3,646 380 33 30 12 30 258 16,915 Boston, MA 301 37 3 3 1 3 26 1,734 Baltimore, MD 3,284 841 65 60 28 65 495 30,930 Newport News, VA 77 12 1 1 0 1 8 532 Savannah, GA 2,578 281 22 20 9 22 169 10,774 Hueneme, CA 52 5 0 0 0 8 3 173 Long Beach, CA 483 67 5 4 2 5 39 2,617


3-62

Table 3-29 Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 (Continued) Metric Tonnes

Port Name


Los Angeles, CA 428 61 4 4 2 5 36 2,379 Oakland, CA 901 104 8 7 3 8 59 3,935 Total RoRo 25,210 3,391 281 259 113 278 2,193 139,396 Total RoRo (short tons) 3,738 310 286 125 306 2,418 153,658


3-63

Table 3-30 Tanker Ship Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Anacortes, WA 455 370 29 26 13 29 207 14,211 Barbers Point, HI 387 108 9 8 4 9 62 4,432 Everett, WA 6 23 2 2 1 2 13 881 Honolulu, HI 687 221 18 16 8 18 130 9,165 Kalama, WA 67 112 9 8 4 9 66 4,579 Longview, WA 30 86 7 6 3 7 49 3,421 Port Angeles, WA 72 34 3 3 1 3 25 1,721 Portland, OR 309 222 19 17 8 18 133 9,246 Seattle, WA 74 152 12 11 5 12 87 5,952 Tacoma, WA 277 1,306 104 94 45 105 723 49,146 Vancouver, WA 133 64 5 5 2 5 37 2,600 Valdez, AK 6,632 338 36 32 11 27 296 20,581 Other Puget Sound 5,678 2,111 219 197 71 169 1,745 118,629 Anchorage, AK 78 45 4 3 2 4 25 1,608 Hilo, HI 11 2 0 0 0 0 1 99 Kahului, HI 9 2 0 0 0 0 1 77 Nawiliwili, HI 8 2 0 0 0 0 1 74 Nikishka, AK 840 189 19 18 6 15 165 10,938 Beaumont, TX 10,807 1,791 228 210 76 159 1,742 71,331 Freeport, TX 5,206 584 80 74 20 45 630 23,551 Galveston, TX 552 68 12 11 2 5 93 2,832 Houston, TX 19,096 2,334 319 294 80 178 2,494 93,908 Port Arthur, TX 1,751 230 31 28 9 19 237 9,232 Texas City, TX 6,856 905 121 111 31 69 942 36,122 Corpus Christi, TX 7,498 1,213 99 91 40 263 752 48,747 Lake Charles, LA 4,544 627 60 55 26 168 450 29,166 Mobile, AL 1,114 187 15 14 6 46 115 7,432 Brownsville, TX 320 45 4 3 1 4 28 1,859 Manatee, FL 355 51 4 4 2 4 32 2,101 Matagorda Ship 1,875 268 22 20 9 22 166 10,961 Panama City, FL 38 5 0 0 0 0 3 204 Pascagoula, MS 2,275 301 26 24 10 25 205 13,293 Tampa, FL 2,282 323 27 24 11 26 202 13,293 Everglades, FL 2,036 495 41 37 15 39 320 21,592 New Orleans, LA 3,506 1,181 96 89 37 92 744 48,739 Baton Rouge, LA 2,603 1,153 93 86 36 90 711 45,874 South Louisiana, LA 5,886 2,187 177 164 68 170 1,365 88,846 Plaquemines, LA 1,322 349 28 26 11 27 221 14,598 Albany, NY 28 8 1 1 0 1 5 326 New York/New Jersey 9,361 1,885 157 144 65 156 1,202 79,931 Portland, ME 2,813 601 49 45 19 47 383 25,538 Hopewell, VA 14 4 0 0 0 0 25 153 Marcus Hook, PA 2,472 904 74 68 28 71 2,255 38,119


3-64

Table 3-30 Tanker Ship Deep Sea Port Emissions in 2002 (continued) Metric Tonnes

Port Name


Morehead City, NC 286 51 4 4 2 4 41 2,169 Paulsboro, NJ 2,952 595 48 45 20 48 2,021 23,560 Fall River, MA 13 3 0 0 0 0 5 120 New Castle, DE 524 147 12 11 5 12 357 6,177 Providence, RI 554 116 10 9 4 9 173 4,907 Brunswick, GA 21 5 0 0 0 0 13 200 Canaveral, FL 351 71 6 5 2 6 55 3,032 Charleston, SC 1,213 260 21 20 8 20 366 11,054 New Haven, CT 951 184 15 14 6 14 131 7,846 Palm Beach, FL 124 22 2 2 1 2 19 923 Bridgeport, CT 206 40 3 3 1 3 29 1,705 Camden, NJ 349 103 8 8 3 8 218 4,257 Philadelphia, PA 2,352 845 70 64 24 67 1,891 36,299 Wilmington, DE 159 38 3 3 1 3 83 1,606 Wilmington, NC 1,167 253 21 19 8 20 375 10,753 Jacksonville, FL 1,633 346 28 26 11 27 419 14,554 Miami, FL 161 33 3 3 1 3 26 1,502 Searsport, ME 498 103 9 8 3 8 114 4,482 Boston, MA 3,775 719 64 59 22 56 757 34,227 New Bedford/Fairhaven, MA 96 22 2 2 1 2 22 924 Baltimore, MD 979 424 33 31 14 33 256 15,951 Newport News, VA 118 21 2 1 1 2 13 840 Savannah, GA 2,083 395 31 29 13 31 258 16,547 Catalina, CA 9 1 0 0 0 0 0 31 Carquinez, CA 1,977 270 20 19 9 21 151 9,920 El Segundo, CA 1,685 192 14 13 6 15 108 7,095 Hueneme, CA 95 13 1 1 0 14 8 547 Long Beach, CA 3,380 419 31 28 13 33 245 16,028 Los Angeles, CA 2,998 383 28 26 12 30 223 14,610 Richmond, CA 2,871 323 24 22 10 25 181 11,904 Sacramento, CA 34 8 1 1 0 1 4 282 San Diego, CA 60 6 0 0 0 0 3 224 San Francisco, CA 1,839 184 14 13 6 14 104 6,823 Stockton, CA 370 79 6 6 3 6 44 2,903 Total Tanker 146,245 29,758 2,796 2,562 994.23952 2,695 27,802 1,259,107 Total Tanker (short tons) 32,802 3,082 2,824 1,096 2,971 30,646 1,387,928


3-65

Table 3-31 Ocean Going Tug Deep Sea Port Emissions in 2002 Metric Tonnes

Port Name


Portland, OR 18 6 1 0 0 0 3 234 Seattle, WA 4 2 0 0 0 0 1 78 Kahului, HI 16 2 0 0 0 0 1 77 Galveston, TX 19 2 0 0 0 0 1 80 Houston, TX 16 1 0 0 0 0 1 70 Corpus Christi, TX 47 5 0 0 0 1 4 226 Lake Charles, LA 7 1 0 0 0 0 1 37 Mobile, AL 46 6 1 0 0 2 4 269 Brownsville, TX 3 0 0 0 0 0 0 16 Manatee, FL 7 1 0 0 0 0 1 49 Pascagoula, MS 7 1 0 0 0 0 1 42 Everglades, FL 28 3 0 0 0 0 2 126 New Orleans, LA 21 4 0 0 0 0 3 198 South Louisiana, LA 7 2 0 0 0 0 1 77 Plaquemines, LA 4 1 0 0 0 0 1 43 New York/New Jersey 3 0 0 0 0 0 0 18 Canaveral, FL 28 3 0 0 0 0 2 135 Palm Beach, FL 28 3 0 0 0 0 2 133 Jacksonville, FL 17 2 0 0 0 0 2 97 Miami, FL 31 3 0 0 0 0 2 152 Boston, MA 4 1 0 0 0 0 0 24 Total Ocean Going Tug 361 48 5 4 2 6 34 2,182 Total Ocean Going Tug (short tons) 53 5 4 2 6 37 2,405


3-66

Table 3-32 Total Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 89 1.5 0.3 0.2 0.0 0.1 2.5 156 Buffalo, NY 84 2.9 0.3 0.3 0.1 0.2 2.3 150 Burns Waterway, IN 819 45.5 3.9 3.6 1.5 3.7 30.0 1,982 Calcite, MI 126 3.4 0.3 0.3 0.1 0.3 2.5 158 Cleveland, OH 560 32.6 2.8 2.5 1.0 2.6 21.8 1,448 Dolomite, MI 67 1.9 0.2 0.1 0.1 0.2 1.1 73 Erie, PA 55 2.2 0.2 0.2 0.1 0.2 1.7 112 Escanaba, MI 118 3.1 0.3 0.3 0.1 0.3 2.3 146 Fairport, OH 114 3.0 0.3 0.3 0.1 0.3 2.5 156 Gary, IN 84 3.2 0.3 0.3 0.1 0.3 2.2 141 Lorain, OH 64 1.5 0.2 0.2 0.1 0.1 1.3 84 Marblehead, OH 26 0.5 0.1 0.1 0.0 0.0 0.5 34 Milwaukee, WI 495 26.1 2.3 2.1 0.8 2.1 17.8 1,177 Muskegon, MI 37 0.9 0.1 0.1 0.0 0.1 0.7 47 Presque Isle, MI 562 16.2 1.4 1.3 0.7 1.4 10.0 637 St Clair, MI 156 4.2 0.4 0.4 0.2 0.4 3.0 193 Stoneport, MI 22 0.7 0.1 0.1 0.0 0.1 0.4 28 Two Harbors, MN 48 1.2 0.1 0.1 0.0 0.1 0.9 56 Ashtabula, OH 1,179 36.8 3.4 3.1 1.3 3.1 26.4 1,688 Chicago, IL 492 22.1 1.9 1.8 0.7 1.8 15.3 1,003 Conneaut, OH 1,863 52.6 5.0 4.7 1.9 4.4 39.5 2,501 Detroit, MI 1,359 51.4 4.7 4.4 1.7 4.2 37.5 2,432 Duluth-Superior, MN&WI 3,441 131.8 12.0 11.1 4.5 10.7 94.5 6,130 Indiana, IN 140 5.9 0.5 0.5 0.2 0.5 4.1 272 Inland Harbor, MI 56 1.5 0.1 0.1 0.1 0.1 1.1 69 Manistee, MI 164 17.8 1.5 1.4 0.5 1.4 12.2 827 Sandusky, OH 742 21.0 2.0 1.8 0.8 1.8 15.2 962 Toledo, OH 1,517 57.9 5.1 4.7 2.0 4.7 39.3 2,550 Total Emissions 14,476 549 50 46 19 45 389 25,210 Total Emissions (short tons) 606 55 50 21 50 429 27,790


3-67

Table 3-33 Auxiliary Engine Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 20 1.2 0.1 0.1 0.0 0.1 0.8 57 Buffalo, NY 19 1.5 0.1 0.1 0.0 0.1 1.0 71 Burns Waterway, IN 181 29.6 2.5 2.2 0.8 2.2 19.7 1,366 Calcite, MI 28 1.4 0.1 0.1 0.0 0.1 0.9 63 Cleveland, OH 122 22.5 1.9 1.7 0.6 1.7 15.0 1,039 Dolomite, MI 15 0.6 0.1 0.0 0.0 0.0 0.4 30 Erie, PA 12 1.5 0.1 0.1 0.0 0.1 1.0 68 Escanaba, MI 26 1.2 0.1 0.1 0.0 0.1 0.8 56 Fairport, OH 25 1.3 0.1 0.1 0.0 0.1 0.9 60 Gary, IN 18 1.5 0.1 0.1 0.0 0.1 1.0 70 Lorain, OH 14 0.7 0.1 0.1 0.0 0.1 0.5 32 Marblehead, OH 6 0.3 0.0 0.0 0.0 0.0 0.2 12 Milwaukee, WI 109 17.5 1.4 1.3 0.5 1.3 11.7 806 Muskegon, MI 8 0.4 0.0 0.0 0.0 0.0 0.3 18 Presque Isle, MI 125 5.6 0.5 0.4 0.2 0.4 3.7 257 St Clair, MI 35 1.6 0.1 0.1 0.0 0.1 1.1 76 Stoneport, MI 5 0.2 0.0 0.0 0.0 0.0 0.2 11 Two Harbors, MN 11 0.5 0.0 0.0 0.0 0.0 0.3 21 Ashtabula, OH 262 16.3 1.3 1.2 0.4 1.2 10.9 752 Chicago, IL 108 13.7 1.1 1.0 0.4 1.0 9.1 633 Conneaut, OH 414 20.9 1.7 1.6 0.6 1.6 13.9 964 Detroit, MI 303 29.6 2.5 2.2 0.8 2.2 19.8 1,367 Duluth-Superior, MN&WI 760 74.0 6.1 5.6 2.0 5.6 49.4 3,418 Indiana, IN 31 3.7 0.3 0.3 0.1 0.3 2.5 170 Inland Harbor, MI 12 0.6 0.0 0.0 0.0 0.0 0.4 27 Manistee, MI 35 15.1 1.3 1.2 0.4 1.2 10.1 699 Sandusky, OH 165 8.1 0.7 0.6 0.2 0.6 5.4 376 Toledo, OH 336 30.9 2.6 2.3 0.9 2.3 20.6 1,426 Total Auxiliary Emissions 3,202 302 25 23 8 23 202 13,944 Total Auxiliary Emissions (short tons) 333 28 25 9 25 222 15,370


3-68

Table 3-34 Cruise Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 89 0.3 0.1 0.1 0.0 0.0 1.2 75 Buffalo, NY 84 0.9 0.1 0.1 0.0 0.1 0.9 55 Burns Waterway, IN 819 11.7 1.0 0.9 0.4 0.9 7.5 453 Calcite, MI 126 1.4 0.1 0.1 0.0 0.1 1.1 66 Cleveland, OH 560 7.7 0.7 0.6 0.3 0.6 5.2 314 Dolomite, MI 67 0.8 0.1 0.1 0.0 0.1 0.5 30 Erie, PA 55 0.6 0.1 0.1 0.0 0.0 0.5 33 Escanaba, MI 118 1.5 0.1 0.1 0.1 0.1 1.2 71 Fairport, OH 114 1.2 0.1 0.1 0.0 0.1 1.1 66 Gary, IN 84 1.1 0.1 0.1 0.0 0.1 0.8 48 Lorain, OH 64 0.6 0.1 0.1 0.0 0.0 0.6 37 Marblehead, OH 26 0.1 0.0 0.0 0.0 0.0 0.3 16 Milwaukee, WI 495 6.4 0.6 0.5 0.2 0.5 4.5 275 Muskegon, MI 37 0.4 0.0 0.0 0.0 0.0 0.3 21 Presque Isle, MI 562 7.3 0.6 0.5 0.2 0.6 4.4 265 St Clair, MI 156 1.7 0.2 0.2 0.1 0.1 1.4 82 Stoneport, MI 22 0.3 0.0 0.0 0.0 0.0 0.2 12 Two Harbors, MN 48 0.6 0.1 0.0 0.0 0.0 0.4 26 Ashtabula, OH 1,179 15.1 1.4 1.3 0.5 1.2 11.3 684 Chicago, IL 492 6.4 0.6 0.5 0.2 0.5 4.6 281 Conneaut, OH 1,863 23.1 2.3 2.1 0.8 1.8 18.4 1,113 Detroit, MI 1,359 16.5 1.6 1.5 0.6 1.3 13.1 793 Duluth-Superior, MN&WI 3,441 43.3 4.2 3.9 1.5 3.4 33.3 2,019 Indiana, IN 140 1.7 0.2 0.2 0.1 0.1 1.3 78 Inland Harbor, MI 56 0.7 0.1 0.1 0.0 0.1 0.5 31 Manistee, MI 164 2.0 0.2 0.2 0.1 0.2 1.6 97 Sandusky, OH 742 9.4 0.9 0.8 0.3 0.7 7.1 428 Toledo, OH 1,517 20.2 1.8 1.7 0.7 1.6 14.0 846 Total Cruise Emissions 14,476 183 17 16 6 14 137 8,313 Total Cruise Emissions (short tons) 202 19 18 7 16 151 9,164


3-69

Table 3-35 Reduced Speed Zone Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 89 0.1 0.0 0.0 0.0 0.0 0.3 19 Buffalo, NY 84 0.2 0.0 0.0 0.0 0.0 0.2 14 Burns Waterway, IN 819 2.8 0.2 0.2 0.1 0.2 1.8 112 Calcite, MI 126 0.3 0.0 0.0 0.0 0.0 0.3 16 Cleveland, OH 560 1.9 0.2 0.1 0.1 0.1 1.3 78 Dolomite, MI 67 0.2 0.0 0.0 0.0 0.0 0.1 7 Erie, PA 55 0.1 0.0 0.0 0.0 0.0 0.1 8 Escanaba, MI 118 0.4 0.0 0.0 0.0 0.0 0.3 18 Fairport, OH 114 0.3 0.0 0.0 0.0 0.0 0.3 16 Gary, IN 84 0.3 0.0 0.0 0.0 0.0 0.2 12 Lorain, OH 64 0.1 0.0 0.0 0.0 0.0 0.1 9 Marblehead, OH 26 0.0 0.0 0.0 0.0 0.0 0.1 4 Milwaukee, WI 495 1.6 0.1 0.1 0.1 0.1 1.1 67 Muskegon, MI 37 0.1 0.0 0.0 0.0 0.0 0.1 5 Presque Isle, MI 562 1.7 0.1 0.1 0.1 0.1 1.0 65 St Clair, MI 156 0.4 0.0 0.0 0.0 0.0 0.3 20 Stoneport, MI 22 0.1 0.0 0.0 0.0 0.0 0.0 3 Two Harbors, MN 48 0.1 0.0 0.0 0.0 0.0 0.1 6 Ashtabula, OH 1,179 3.7 0.3 0.3 0.1 0.3 2.8 170 Chicago, IL 492 1.5 0.1 0.1 0.1 0.1 1.1 69 Conneaut, OH 1,863 5.8 0.6 0.5 0.2 0.5 4.5 277 Detroit, MI 1,359 4.1 0.4 0.4 0.1 0.3 3.1 193 Duluth-Superior, MN&WI 3,441 10.8 1.0 0.9 0.4 0.8 8.1 502 Indiana, IN 140 0.4 0.0 0.0 0.0 0.0 0.3 19 Inland Harbor, MI 56 0.2 0.0 0.0 0.0 0.0 0.1 7 Manistee, MI 164 0.5 0.0 0.0 0.0 0.0 0.4 24 Sandusky, OH 742 2.3 0.2 0.2 0.1 0.2 1.7 106 Toledo, OH 1,517 4.9 0.4 0.4 0.2 0.4 3.4 208 Total RSZ Emissions 14,476 45 4 4 2 4 33 2,052 Total RSZ Emissions (short tons) 50 5 4 2 4 37 2,262


3-70

Table 3-36 Maneuvering Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 89 0.2 0.0 0.0 0.0 0.0 0.3 17 Buffalo, NY 84 0.6 0.1 0.1 0.0 0.1 0.4 28 Burns Waterway, IN 819 4.4 0.4 0.4 0.3 0.5 3.0 190 Calcite, MI 126 0.9 0.1 0.1 0.1 0.1 0.6 41 Cleveland, OH 560 2.0 0.2 0.2 0.1 0.2 1.4 89 Dolomite, MI 67 0.5 0.0 0.0 0.0 0.1 0.3 19 Erie, PA 55 0.2 0.0 0.0 0.0 0.0 0.2 11 Escanaba, MI 118 0.3 0.0 0.0 0.0 0.0 0.2 15 Fairport, OH 114 0.8 0.1 0.1 0.0 0.1 0.6 38 Gary, IN 84 0.7 0.1 0.1 0.0 0.1 0.5 32 Lorain, OH 64 0.4 0.0 0.0 0.0 0.0 0.3 18 Marblehead, OH 26 0.1 0.0 0.0 0.0 0.0 0.1 6 Milwaukee, WI 495 2.3 0.2 0.2 0.1 0.3 1.6 105 Muskegon, MI 37 0.2 0.0 0.0 0.0 0.0 0.2 11 Presque Isle, MI 562 4.2 0.4 0.4 0.3 0.5 2.6 167 St Clair, MI 156 1.1 0.1 0.1 0.1 0.1 0.7 47 Stoneport, MI 22 0.2 0.0 0.0 0.0 0.0 0.1 7 Two Harbors, MN 48 0.2 0.0 0.0 0.0 0.0 0.2 10 Ashtabula, OH 1,179 6.0 0.6 0.6 0.3 0.7 4.4 277 Chicago, IL 492 2.0 0.2 0.2 0.1 0.2 1.4 87 Conneaut, OH 1,863 9.7 1.0 0.9 0.6 1.1 7.3 466 Detroit, MI 1,359 5.7 0.6 0.6 0.3 0.6 4.5 284 Duluth-Superior, MN&WI 3,441 15.2 1.6 1.5 0.9 1.7 11.3 718 Indiana, IN 140 0.5 0.1 0.0 0.0 0.1 0.4 24 Inland Harbor, MI 56 0.3 0.0 0.0 0.0 0.0 0.2 13 Manistee, MI 164 0.6 0.1 0.1 0.0 0.1 0.4 27 Sandusky, OH 742 3.8 0.4 0.4 0.2 0.4 2.7 174 Toledo, OH 1,517 6.6 0.7 0.6 0.4 0.8 4.6 291 Total Maneuver Emissions 14,476 70 7 7 4 8 50 3,213 Total Maneuver Emissions (short tons) 77 8 7 5 9 56 3,542


3-71

Table 3-37 Hotelling Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 89 1.0 0.1 0.1 0.0 0.1 0.7 46 Buffalo, NY 84 1.2 0.1 0.1 0.0 0.1 0.8 53 Burns Waterway, IN 819 26.6 2.2 2.0 0.7 2.0 17.7 1,227 Calcite, MI 126 0.8 0.1 0.1 0.0 0.1 0.5 36 Cleveland, OH 560 20.9 1.7 1.6 0.6 1.6 14.0 967 Dolomite, MI 67 0.4 0.0 0.0 0.0 0.0 0.2 16 Erie, PA 55 1.3 0.1 0.1 0.0 0.1 0.9 60 Escanaba, MI 118 0.9 0.1 0.1 0.0 0.1 0.6 42 Fairport, OH 114 0.8 0.1 0.1 0.0 0.1 0.5 36 Gary, IN 84 1.1 0.1 0.1 0.0 0.1 0.7 49 Lorain, OH 64 0.4 0.0 0.0 0.0 0.0 0.3 20 Marblehead, OH 26 0.2 0.0 0.0 0.0 0.0 0.1 8 Milwaukee, WI 495 15.8 1.3 1.2 0.4 1.2 10.5 730 Muskegon, MI 37 0.2 0.0 0.0 0.0 0.0 0.2 11 Presque Isle, MI 562 3.0 0.3 0.2 0.1 0.2 2.0 140 St Clair, MI 156 1.0 0.1 0.1 0.0 0.1 0.6 44 Stoneport, MI 22 0.1 0.0 0.0 0.0 0.0 0.1 6 Two Harbors, MN 48 0.3 0.0 0.0 0.0 0.0 0.2 13 Ashtabula, OH 1,179 12.1 1.0 0.9 0.3 0.9 8.0 557 Chicago, IL 492 12.2 1.0 0.9 0.3 0.9 8.2 565 Conneaut, OH 1,863 14.0 1.2 1.1 0.4 1.1 9.3 645 Detroit, MI 1,359 25.1 2.1 1.9 0.7 1.9 16.8 1,162 Duluth-Superior, MN&WI 3,441 62.6 5.2 4.8 1.7 4.8 41.8 2,891 Indiana, IN 140 3.3 0.3 0.2 0.1 0.2 2.2 152 Inland Harbor, MI 56 0.4 0.0 0.0 0.0 0.0 0.3 18 Manistee, MI 164 14.7 1.2 1.1 0.4 1.1 9.8 679 Sandusky, OH 742 5.5 0.5 0.4 0.2 0.4 3.7 254 Toledo, OH 1,517 26.1 2.2 2.0 0.7 2.0 17.4 1,205 Total Hotel Emissions 14,476 252 21 19 7 19 168 11,631 Total Hotel Emissions (short tons) 278 23 21 8 21 185 12,821


3-72

Table 3-38 Self-Unloading Bulk Carrier Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Alpena, MI 89 1.5 0.3 0.2 0.0 0.1 2.5 156 Buffalo, NY 71 2.0 0.2 0.2 0.1 0.2 1.8 112 Burns Waterway, IN 236 7.6 0.7 0.7 0.3 0.7 5.7 362 Calcite, MI 126 3.4 0.3 0.3 0.1 0.3 2.5 158 Cleveland, OH 75 1.3 0.2 0.2 0.0 0.1 1.7 109 Dolomite, MI 67 1.9 0.2 0.1 0.1 0.2 1.1 73 Erie, PA 27 0.5 0.1 0.1 0.0 0.0 0.6 37 Escanaba, MI 118 3.1 0.3 0.3 0.1 0.3 2.3 146 Fairport, OH 114 3.0 0.3 0.3 0.1 0.3 2.5 156 Gary, IN 71 2.3 0.2 0.2 0.1 0.2 1.6 104 Lorain, OH 64 1.5 0.2 0.2 0.1 0.1 1.3 84 Marblehead, OH 26 0.5 0.1 0.1 0.0 0.0 0.5 34 Milwaukee, WI 169 4.3 0.5 0.4 0.2 0.4 3.8 238 Muskegon, MI 37 0.9 0.1 0.1 0.0 0.1 0.7 47 Presque Isle, MI 562 16.2 1.4 1.3 0.7 1.4 10.0 637 St Clair, MI 156 4.2 0.4 0.4 0.2 0.4 3.0 193 Stoneport, MI 22 0.7 0.1 0.1 0.0 0.1 0.4 28 Two Harbors, MN 48 1.2 0.1 0.1 0.0 0.1 0.9 56 Ashtabula, OH 1,047 29.2 2.8 2.6 1.1 2.5 21.6 1,363 Chicago, IL 208 5.5 0.5 0.5 0.2 0.5 4.4 282 Conneaut, OH 1,843 51.3 4.9 4.6 1.9 4.3 38.7 2,449 Detroit, MI 802 20.3 2.1 2.0 0.7 1.7 17.5 1,106 Duluth-Superior, MN&WI 2,201 61.8 6.0 5.6 2.3 5.2 47.9 3,034 Indiana, IN 52 1.3 0.1 0.1 0.0 0.1 1.1 68 Inland Harbor, MI 56 1.5 0.1 0.1 0.1 0.1 1.1 69 Manistee, MI 9 0.2 0.0 0.0 0.0 0.0 0.2 11 Sandusky, OH 735 20.6 1.9 1.8 0.8 1.7 14.8 935 Toledo, OH 987 28.1 2.5 2.3 1.0 2.4 19.3 1,226 Total SU Bulk Carrier 10,015 276 27 25 10 23 210 13,273 Total SU Bulk Carrier (short tons) 304 29 27 11 26 231 14,631


3-73

Table 3-39 Bulk Carrier Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Buffalo, NY 13 0.9 0.1 0.1 0.0 0.1 0.6 38 Burns Waterway, IN 562 36.8 3.0 2.8 1.1 2.9 23.5 1,567 Cleveland, OH 427 27.7 2.3 2.1 0.9 2.2 17.7 1,179 Erie, PA 17 1.1 0.1 0.1 0.0 0.1 0.7 46 Gary, IN 7 0.5 0.0 0.0 0.0 0.0 0.3 22 Milwaukee, WI 292 19.9 1.6 1.5 0.6 1.6 12.7 852 Ashtabula, OH 126 7.4 0.6 0.6 0.2 0.6 4.7 313 Chicago, IL 219 12.8 1.1 1.0 0.4 1.0 8.3 550 Conneaut, OH 20 1.2 0.1 0.1 0.0 0.1 0.8 52 Detroit, MI 458 27.2 2.2 2.1 0.9 2.2 17.3 1,149 Duluth-Superior, MN&WI 1,032 61.1 5.2 4.7 1.9 4.8 40.5 2,692 Indiana, IN 88 4.6 0.4 0.4 0.1 0.4 3.1 203 Sandusky, OH 7 0.4 0.0 0.0 0.0 0.0 0.4 27 Toledo, OH 421 25.1 2.1 2.0 0.8 2.0 16.8 1,116 Total Bulk Carrier 3,689 227 19 17 7 18 147 9,807 Total Bulk Carrier (short tons) 250 21 19 8 20 162 10,811

Table 3-40 General Cargo Ship Emissions by Great Lake Port in 2002

Metric Tonnes

Port Name


Burns Waterway, IN 21 1.2 0.1 0.1 0.0 0.1 0.8 53 Cleveland, OH 58 3.5 0.3 0.3 0.1 0.3 2.4 160 Erie, PA 11 0.6 0.1 0.1 0.0 0.0 0.4 29 Milwaukee, WI 34 1.9 0.2 0.2 0.1 0.2 1.3 87 Ashtabula, OH 6 0.2 0.0 0.0 0.0 0.0 0.2 12 Chicago, IL 44 1.9 0.2 0.2 0.1 0.2 1.3 84 Detroit, MI 44 2.0 0.2 0.2 0.1 0.2 1.3 88 Duluth-Superior, MN&WI 167 6.7 0.6 0.5 0.2 0.5 4.7 305 Toledo, OH 77 3.5 0.3 0.3 0.1 0.3 2.3 152 Total General Cargo 462 22 2 2 1 2 15 969 Total General Cargo (short tons) 24 2 2 1 2 16 1,068

Table 3-41 Tanker Ship Emissions by Great Lake Port in 2002

Metric Tonnes

Port Name


Chicago, IL 15 1.8 0.1 0.1 0.1 0.1 1.2 80 Detroit, MI 6 0.7 0.1 0.1 0.0 0.1 0.4 30 Duluth-Superior, MN&WI 12 1.4 0.1 0.1 0.0 0.1 0.9 63 Manistee, MI 155 17.6 1.5 1.4 0.5 1.4 12.1 816 Toledo, OH 5 0.5 0.0 0.0 0.0 0.0 0.3 24 Total Tanker 193 22 2 2 1 2 15 1,012 Total Tanker (short tons) 24 2 2 1 2 16 1,116


3-74

Table 3-42 Integrated Tug-Barge Emissions by Great Lake Port in 2002 Metric Tonnes

Port Name


Gary, IN 6 0.3 0.0 0.0 0.0 0.0 0.2 15 Chicago, IL 6 0.1 0.0 0.0 0.0 0.0 0.1 7 Detroit, MI 49 1.2 0.1 0.1 0.0 0.1 0.9 59 Duluth-Superior, MN&WI 29 0.7 0.1 0.1 0.0 0.1 0.6 35 Toledo, OH 27 0.7 0.1 0.1 0.0 0.1 0.5 32 Total ITB 117 3 0 0 0 0 2 149 Total ITB (short tons) 3 0 0 0 0 3 164

For Great Lake ports, auxiliary emissions are responsible for roughly 50% of the NOX and PM emissions, primarily due to emissions during the hotelling mode. Bulk Carrier ships are responsible for the vast majority of the emissions.

3.3.2.6.3 Summary

This section provides a summary of the total port emissions for 2002. Table 3-43 and Table 3-44 provide a breakout of the total port emissions by auxiliary and propulsion engines, in units of metric tonnes and short tons, respectively. Table 3-45 and Table 3-46 provide the breakout by mode of operation, while Table 3-47 and Table 3-48 provide a summary of port emissions by ship type.

Auxiliary emissions at ports are responsible for 39-48% of the total inventory, depending on the pollutant. Hotelling, cruise, and RSZ modes of operation are all important contributors to emissions. Container and Tanker ships are the largest contributors to port emissions.

Table 3-43 2002 Port Emissions Summary by Engine and Port Type (metric tonnes) Metric Tonnes

Engine Type Port Type NOX PM10 PM2.5 HC CO SO2 CO2 Deep Sea 64,288 5,478 5,034 2,532 6,329 52,676 2,360,435 Great Lakes 248 25 23 11 22 187 11,267 Propulsion Total 64,536 5,503 5,057 2,543 6,351 52,863 2,371,702 Deep Sea 57,317 5,052 4,597 1,615 4,306 41,232 2,635,436 Great Lakes 302 25 23 8 23 202 13,944 Auxiliary Total 57,619 5,077 4,620 1,624 4,328 41,433 2,649,380 Deep Sea 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 Great Lakes 549 50 46 19 45 389 25,210 All Grand Total 122,155 10,580 9,677 4,167 10,680 94,297 5,021,082


3-75

Table 3-44 2002 Port Emissions Summary by Engine and Port Type (short tons)

Short Tons Engine Type Port Type NOX PM10 PM2.5 HC CO SO2 CO2

Deep Sea 70,866 6,039 5,549 2,792 6,977 58,065 2,601,934 Great Lakes 273 27 25 12 24 207 12,419 Propulsion Total 71,139 6,066 5,575 2,803 7,001 58,272 2,614,353 Deep Sea 63,181 5,569 5,067 1,781 4,746 45,450 2,905,071 Great Lakes 333 28 25 9 25 222 15,370 Auxiliary Total 63,514 5,597 5,092 1,790 4,771 45,672 2,920,442 Deep Sea 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 Great Lakes 606 55 50 21 50 429 27,790 All Grand Total 134,653 11,662 10,667 4,593 11,772 103,944 5,534,795

Table 3-45 2002 Port Emissions Summary by Mode and Port Type (metric tonnes)

Metric Tonnes Mode Port Type NOX PM10 PM2.5 HC CO SO2 CO2

Deep Sea 34,193 2,826 2,623 1,141 2,651 21,186 1,314,146

Great Lakes 183 17 16 6 14 137 8,313 Cruise

Total 34,376 2,843 2,639 1,148 2,665 21,323 1,322,459

Deep Sea 34,427 2,887 2,657 1,280 3,804 35,148 1,318,897

Great Lakes 45 4 4 2 4 33 2,052 RSZ

Total 34,472 2,891 2,661 1,281 3,808 35,181 1,320,950

Deep Sea 7,383 758 625 440 724 4,356 266,262

Great Lakes 70 7 7 4 8 50 3,213 Maneuvering

Total 7,452 765 632 444 732 4,406 269,476

Deep Sea 45,603 4,060 3,726 1,287 3,456 33,218 2,096,566

Great Lakes 252 21 19 7 19 168 11,631 Hotelling

Total 45,855 4,081 3,745 1,294 3,475 33,386 2,108,197 Deep Sea 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 Great Lakes 549 50 46 19 45 389 25,210 All

Grand Total 122,155 10,580 9,677 4,167 10,680 94,297 5,021,082


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Table 3-46 2002 Port Emissions Summary by Mode and Port Type (short tons)

Short Tons Mode Port Type NOX PM10 PM2.5 HC CO SO2 CO2

Deep Sea 37,691 3,115 2,891 1,258 2,922 23,353 1,448,598

Great Lakes 202 19 18 7 16 151 9,164 Cruise

Total 37,893 3,134 2,909 1,265 2,938 23,504 1,457,762

Deep Sea 37,949 3,182 2,929 1,410 4,193 38,744 1,453,835

Great Lakes 50 5 4 2 4 37 2,262 RSZ

Total 37,999 3,187 2,934 1,412 4,197 38,781 1,456,098

Deep Sea 8,138 835 689 485 799 4,802 293,504

Great Lakes 77 8 7 5 9 56 3,542 Maneuvering

Total 8,215 843 696 490 807 4,857 297,046

Deep Sea 50,268 4,475 4,107 1,419 3,809 36,617 2,311,068

Great Lakes 278 23 21 8 21 185 12,821 Hotelling

Total 50,546 4,498 4,128 1,426 3,830 36,802 2,323,889 Deep Sea 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 Great Lakes 606 55 50 21 50 429 27,790 All

Grand Total 134,653 11,662 10,667 4,593 11,772 103,944 5,534,795


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Table 3-47 2002 Port Emissions Summary by Ship Type and Port Type (metric tonnes)

Metric Tonnes Ship Type Port Type NOX PM10 PM2.5 HC CO SO2 CO2

Deep Sea 5,125 421 384 185 577 3,676 198,637 Great Lakes 0 0 0 0 0 0 0 Auto Carrier

Total 5,125 421 384 185 577 3,676 198,637 Deep Sea 148 13 12 5 12 141 6,364 Great Lakes 0 0 0 0 0 0 0 Barge Carrier Total 148 13 12 5 12 141 6,364 Deep Sea 0 0 0 0 0 0 0 Great Lakes 276 27 25 10 23 210 13,273

Self-Unloading Bulk Carrier

Total 276 27 25 10 23 210 13,273 Deep Sea 19,373 1,570 1,431 633 1,732 14,945 767,825 Great Lakes 227 19 17 7 18 147 9,807

Other Bulk Carrier

Total 19,600 1,589 1,448 640 1,750 15,092 777,632 Deep Sea 33,990 2,733 2,494 1,282 2,833 22,628 1,288,596 Great Lakes 0 0 0 0 0 0 0 Container Total 33,990 2,733 2,494 1,282 2,833 22,628 1,288,596 Deep Sea 7,402 630 576 251 684 6,208 302,338 Great Lakes 22 2 2 1 2 15 969 General Cargo Total 7,424 631 578 252 686 6,223 303,307 Deep Sea 179 16 15 6 35 128 8,209 Great Lakes 0 0 0 0 0 0 0 Miscellaneous Total 179 16 15 6 35 128 8,209 Deep Sea 19,165 1,819 1,668 578 1,470 14,184 893,157 Great Lakes 0 0 0 0 0 0 0 Passenger Total 19,165 1,819 1,668 578 1,470 14,184 893,157 Deep Sea 3,027 247 226 98 313 1,968 130,060 Great Lakes 0 0 0 0 0 0 0

Refrigerated Cargo

Total 3,027 247 226 98 313 1,968 130,060 Deep Sea 3,391 281 259 113 278 2,193 139,396 Great Lakes 0 0 0 0 0 0 0

Roll-On/Roll-Off

Total 3,391 281 259 113 278 2,193 139,396 Deep Sea 29,758 2,796 2,562 994 2,695 27,802 1,259,107 Great Lakes 22 2 2 1 2 15 1,012 Tanker Total 29,780 2,798 2,564 995 2,697 27,817 1,260,119 Deep Sea 48 5 4 2 6 34 2,182 Great Lakes 0 0 0 0 0 0 0

Ocean Going Tug

Total 48 5 4 2 6 34 2,182 Deep Sea 0 0 0 0 0 0 0 Great Lakes 3 0 0 0 0 2 149

Integrated Tug-Barge

Total 3 0 0 0 0 2 149 Deep Sea 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 Great Lakes 549 50 46 19 45 389 25,210 All Grand Total 122,155 10,580 9,677 4,167 10,680 94,297 5,021,082


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Table 3-48 2002 Port Emissions Summary by Ship Type and Port Type (short tons) Short Tons

Ship Type Port Type NOX PM10 PM2.5 HC CO SO2 CO2

Deep Sea 5,649 464 424 204 636 4,052 218,960 Great Lakes 0 0 0 0 0 0 0 Auto Carrier

Total 5,649 464 424 204 636 4,052 218,960 Deep Sea 163 14 13 6 14 156 7,015 Great Lakes 0 0 0 0 0 0 0 Barge Carrier

Total 163 14 13 6 14 156 7,015 Deep Sea 0 0 0 0 0 0 0 Great Lakes 304 29 27 11 26 231 14,631

Self-Unloading Bulk Carrier

Total 304 29 27 11 26 231 14,631 Deep Sea 21,355 1,731 1,577 697 1,909 16,474 846,382 Great Lakes 250 21 19 8 20 162 10,811

Other Bulk Carrier

Total 21,605 1,752 1,597 705 1,929 16,636 857,193 Deep Sea 37,468 3,012 2,749 1,413 3,123 24,944 1,420,434 Great Lakes 0 0 0 0 0 0 0 Container

Total 37,468 3,012 2,749 1,413 3,123 24,944 1,420,434 Deep Sea 8,159 694 635 277 754 6,843 333,270 Great Lakes 24 2 2 1 2 16 1,068 General Cargo

Total 8,183 696 637 278 756 6,860 334,338 Deep Sea 197 18 17 7 39 141 9,049 Great Lakes 0 0 0 0 0 0 0 Miscellaneous

Total 197 18 17 7 39 141 9,049 Deep Sea 21,126 2,005 1,838 638 1,620 15,635 984,538 Great Lakes 0 0 0 0 0 0 0 Passenger

Total 21,126 2,005 1,838 638 1,620 15,635 984,538 Deep Sea 3,337 273 249 108 345 2,170 143,367 Great Lakes 0 0 0 0 0 0 0

Refrigerated Cargo

Total 3,337 273 249 108 345 2,170 143,367 Deep Sea 3,738 310 286 125 306 2,418 153,658 Great Lakes 0 0 0 0 0 0 0

Roll-On/Roll-Off

Total 3,738 310 286 125 306 2,418 153,658 Deep Sea 32,802 3,082 2,824 1,096 2,971 30,646 1,387,928 Great Lakes 24 2 2 1 2 16 1,116 Tanker

Total 32,826 3,084 2,826 1,097 2,973 30,663 1,389,044 Deep Sea 53 5 4 2 6 37 2,405 Great Lakes 0 0 0 0 0 0 0

Ocean Going Tug

Total 53 5 4 2 6 37 2,405 Deep Sea 0 0 0 0 0 0 0 Great Lakes 3 0 0 0 0 3 164

Integrated Tug-Barge

Total 3 0 0 0 0 3 164 Deep Sea 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 Great Lakes 606 55 50 21 50 429 27,790 All

Grand Total 134,653 11,662 10,667 4,593 11,772 103,944 5,534,795


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3.3.3 Interport Emissions

This section presents our nationwide analysis of the methodology and inputs used to estimate interport emissions from main propulsion and auxiliary engines used by Category 3 ocean-going vessels for the 2002 calendar year. The modeling domain for vessels operating in the ocean extends from the U.S. coastline to a 200 nautical mile boundary. For ships operating in the Great Lakes, it extends out to the international boundary with Canada. The emission results are divided into nine geographic regions of the U.S. (including Alaska and Hawaii), and then totaled to provide a national inventory.

The interport emissions described in this section represent total interport emissions prior to any adjustments made to incorporate near-port inventories. The approach used to replace the near-port portion of the interport emissions is provided in Section 3.3.3.3. The final adjusted interport emissions are provided in Section 3.3.4.

3.3.3.1 Methodology

The interport emissions were estimated using the Waterway Network Ship Traffic, Energy, and Environmental Model (STEEM).3,4 STEEM was developed by the University of Delaware as a comprehensive approach to quantify and geographically represent interport ship traffic, emissions, and energy consumption from large vessels calling on U.S. ports or transiting the U.S. coastline to other destinations, and shipping activity in Canada and Mexico. The model estimates emissions from main propulsion and auxiliary marine engines used on Category 3 vessels that engage in foreign commerce using historical North American shipping activity, ship attributes (i.e., characteristics), and activity-based emission factor information. These inputs are assembled using a geographic information system (GIS) platform that also contains an empirically derived network of shipping lanes. It includes the emissions for all ship operational modes from cruise in unconstrained shipping lanes to maneuvering in a port. The model, however, excludes hotelling operations while the vessel is docked or anchored, and very low speed maneuvering close to a dock. For that reason, STEEM is referred to as an “interport” model, to easily distinguish it from the near ports analysis.

STEEM uses advanced ArcGIS tools and develops emission inventories in the following way.39 The model begins by building a spatially-defined waterway network based on empirical shipping location information from two global ship reporting databases. The first is the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), which contains reports on marine surface and atmospheric conditions from the Voluntary Observing Ships (VOS) fleet. There are approximately 4,000 vessels worldwide in the VOS system. The ICOADS project is sponsored by the National Oceanic and Atmospheric Administration and National Science Foundation's National Center for Atmospheric Research (NCAR). The second database is the Automated Mutual-Assistance Vessel Rescue (AMVER) system. The AMVER data set is based on a ship search and rescue reporting network sponsored by the U.S. Coast Guard. The AMVER system is also voluntary, but is generally limited to ships over 1,000 gross tons on voyages of 24 hours or longer. About 8,600 vessels reported to AMVER in 2004.


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The latitude and longitude coordinates for the ship reports in the above databases are used to statistically create and spatially define the direction and width of each shipping lane in the waterway network. Each statistical lane (route and segment) is given a unique identification number for computational purposes. For the current analysis, STEEM used 20 years of ICOADS data (1983-2002) and about one year of AMVER data (part of 2004 and part of 2005) (Figure 3-2).

Figure 3-2 AMVER and ICOADS data

Every major ocean and Great Lake port is also spatially located in the waterway network

using ArcGIS software. For the U.S., the latitude and longitude for each port is taken from the USACE report on vessel entrances and clearances.13 There are 251 U.S. ports in the USACE entrances and clearances report. Each port also has a unique identification number for computational purposes.

As illustrated in Figure 3-3, the waterway network represented by STEEM resembles a

highway network on land. It is composed of ports, which are origins and destinations of shipping routes: junctions where shipping routes intersect, and segments that are shipping lanes between two connected junctions. Each segment can have only two junctions or ports, and ship traffic flow can enter and leave a segment only through a junction or at a port. The figure represents only a sample of the many routes contained in the model.

Deleted: -3


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Figure 3-3 Illustration of STEEM Modeling Domain and Spatial Distribution of Shipping Lanes

Every major ocean and Great Lake port is also spatially located in the waterway network using ArcGIS software. For the U.S., the latitude and longitude for each port is taken from the U.S. Army Corps of Engineers report on vessel entrances and clearances (subsequently referred to as USACE).13 There are 251 U.S. ports in the entrances and clearances report. Each port also has a unique identification number for computational purposes.

The STEEM interport model also employs a number of databases to identify the movements for each vessel (e.g., trips), individual ship attributes (e.g., vessel size and horsepower), and related emission factor information (e.g., emission rates) that are subsequently used in the inventory calculations.

Once the waterway network and various databases are constructed, STEEM uses ArcGIS Network Analyst tools along with specific information on each individual ship movement to solve the most probable path on the network between each pair of ports (i.e., a trip) for a certain ship size. This is assumed to represent the least-energy path, which in most cases is the shortest distance unless prevented by weather or sea conditions, water depth, channel width, navigational regulations, or other constraints that are beyond the model’s capability to forecast.

After identifying the shipping route and resulting distance associated with each unique trip, the emissions are simply calculated for each operational mode using the following generalized equation along with information from the ship attributes and emission factor databases:

Emissions per trip = distance (nautical miles) / speed (nautical miles/hour) x horsepower (kW) x fractional load factor x emission factor (g/kW-hour)


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In STEEM, emissions are calculated separately for distances representing cruise and

maneuvering operational modes. Maneuvering occurs at slower speeds and load factors than during cruise conditions. In STEEM, maneuvering is assumed to occur for the first and last 20 kilometers of each trip when a ship is entering or leaving a port. A ship is assumed to move at maneuvering speed for an entire trip if the distance is less than 20 kilometers.

Finally, the emissions along each shipping route (i.e., segment) for all trips are proportioned among the respective cells that are represented by the gridded modeling domain. For this work, emissions estimates were produced at a cell resolution of 4 kilometers by 4 kilometers, which is appropriate for most atmospheric air quality models. The results for each cell are then summed, as appropriate, to produce emission inventories for the various geographic regions of interest in this analysis.

3.3.3.1.1 Emission Inputs

The STEEM waterway network model relies on a number of inputs to identify the movements for each vessel, individual ship attributes, and related emission factor information. Each of these databases is described separately below.

3.3.3.1.1.1 Shipping Movements

The shipping activity and routes database provides information on vessel movements or trips. It is developed using port entrances and clearances information from the USACE report for the U.S. and the Lloyd’s Maritime Intelligence Unit (LMIU) for Canada and Mexico.40 These sources contain information for each vessel carrying foreign cargo at each major port or waterway that, most importantly for this analysis, includes:

• Vessel name • Last port of call (entrance record) or next port of call (clearance record)

The database then establishes unique identification numbers for each ship, each port pair,

and each resulting trip.

3.3.3.1.1.2 Ship Attributes

The ship attributes data set contains the important characteristics of each ship that are necessary for the STEEM interport model to calculate the emissions associated with each trip. The information in this data set is matched to each previously assigned ship identification number. The following information comes from the USACE entrances and clearances report for each ship identification number:

• Ship type • Gross registered tonnage (GRT) • Net registered tonnage (NRT)


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The ship attributes data set contains the following information from Lloyd’s Register-Fairplay for each ship identification number.14

• Main propulsion engine installed power (horsepower) • Service speed (cruise speed) • Ship size (length, wide, and draft)

Sometimes data was lacking from the above references for ship speed. In these instances,

the missing information was developed for each of nine vessel types and the appropriate value was applied to each individual ship of that type. Specifically, the missing ship speeds for each ship category were obtained from the average speeds used in a Lloyd’s Register study of the Baltic Sea and from an Entec UK Limited study for the European Commission.41,21 The resulting vessel cruise speeds for ships with missing data are shown in Table 3-49.

Table 3-49 Average Vessel Cruise Speed by Ship Type a Ship Type Average Cruise Speed (knots)

Bulk Carrier 14.1 Container Ship 19.9 General Cargo 12.3 Passenger Ship 22.4 Refrigerated Cargo 16.4 Roll On-Roll Off 16.9 Tanker 13.2 Fishing 11.7 Miscellaneous 12.7

Note: a Used only when ship specific data were missing from the commercial database references.

The average speed during maneuvering is approximately 60 percent of a ship’s cruise

speed based on using the propeller law described in Section 3.3.2 above and the engine load factor for maneuvering that is presented later in this section.

As with vessel cruise speed, main engine installed power was sometimes lacking in the Lloyd’s Register-Fairplay data set. Here again, the missing information was developed for nine different vessel types and the appropriate value was applied to each individual ship of that type when the data were lacking. In this case, the missing main engine horsepower was estimated by regressing the relationships between GRT and NRT, and between installed power and GRT for each category. This operation is performed internally in the model and the result applied to each individual ship, as appropriate.

The ship attributes database also contains information on the installed power of engines used for auxiliary purposes. However, this information is usually lacking in the Lloyds data set, so an alternative technique was employed to estimate the required values. In short, the STEEM model uses a ratio of main engine horsepower to auxiliary engine horsepower that was determined for eight different vessel types using information primarily from ICF International.42


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(The ICF report attributed these power values to a study for the Port of Los Angeles by Starcrest Consulting.12) The auxiliary engine power for each individual vessel of a given ship type is then estimated by multiplying the appropriate main power to auxiliary power ratio and the main engine horsepower rating for that individual ship. The main and auxiliary power values and the resulting auxiliary engine to main engine ratios are shown in Table 3-50.

Table 3-50 Auxiliary Engine Power Ratios

Vessel Type

Average Main Engine Power

(kW) Average Auxiliary

Engine Power (kW)

Auxiliary to Main Engine Power

Ratio Bulk Carrier 7,954 1,169 0.147 Container Ship 30,885 5,746 0.186 General Cargo 9,331 1,777 0.190 Passenger Ship 39,563 39,563 a 1.000 Refrigerated Cargo 9,567 3,900 b 0.136 Roll On-Roll Off 10,696 c 2,156 c 0.202 Tanker 9,409 1,985 0.211 Miscellaneous 6,252 1,680 0.269

Notes: a The ICF reference reported a value of 11,000 for auxiliary engines used on passenger vessels.42 b The STEEM used auxiliary engine power as reported in the ARB methodology document.36

c The STEEM purportedly used values for Roll On-Roll Off main and auxiliary engines that represent a trip weighted average of the Auto Carrier and Cruise Ship power values from the ICF reference.

Finally, the ship attributes database provides information on the load factors for main

engines during cruise and maneuvering operation, in addition to load factors for auxiliary marine engines. Main engine load factors for cruise operation were taken from a study of international shipping for all ship types, except passenger vessels.43 For this analysis, the STEEM model used a propulsion engine load factor for passenger ship engines at cruise speed of 55 percent of the total installed power. This is based on engine manufacturer data contained in two global shipping studies.43,44 During maneuvering, it was assumed that all main engines, including those for passenger ships, operate at 20 percent of the installed power. This is consistent with a study done by Entec UK for the European Commission.21 The main engine load factors at cruise speed by ship type are shown in Table 3-51.

Auxiliary engine load factors, except for passenger ships, were obtained from the ICF International study referenced above. These values are also shown in Table 3-51. For cruise mode, neither port nor interport portions of the inventory were adjusted for low load operation, as the low load adjustments are only applied to propulsion engines with load factors below 20%.


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Table 3-51 Main and Auxiliary Engine Load Factors at Cruise Speed by Ship Type

Ship Type Average Main Engine

Load Factor (%) Average Auxiliary Engine

Load Factor (%) Bulk Carrier 75 17 Container Ship 80 13 General Cargo 80 17 Passenger Ship 55 25 Refrigerated Cargo 80 20 Roll On-Roll Off 80 15 Tanker 75 13 Miscellaneous 70 17

3.3.3.1.1.3 Emission Factor Information

The emission factor data set contains emission rates for the various pollutants in terms of grams of pollutant per kilowatt-hour (g/kW-hr). The main engine emission factors are shown in Table 3-52 . The speed specific factors for NOX, HC, and SO2 were taken from several recent analyses of ship emissions in the U.S., Canada, and Europe. 21,36,42,43, 45 The PM factor was based on discussions with the California Air Resources Board (ARB) staff. The fuel specific CO emission factor was taken from a report by ENVIRON International.19 The STEEM study used the composite emission factors shown in the table because the voyage data used in the model do not explicitly identify main engine speed ratings, i.e., slow or medium, or the auxiliary engine fuel type, i.e., marine distillate or residual marine. The composite factor for each pollutant is determined by weighting individual emission factors by vessel engine population data from a 2005 survey of ocean-going vessels that was performed by ARB.20 Fuel consumption was calculated from CO2 emissions using the same ratio (1:3.183) as used in the near-port analysis.

Table 3-52 Main Engine Emission Factors by Ship and Fuel Type Main Engine Emission Factors (g/kW-hr)

Engine Type Fuel Type NOX PM10 PM 2.5 a HC CO SO2

Slow Speed Residual Marine 18.1 1.5 1.4 0.6 1.4 10.5

Medium Speed Residual Marine 14 1.5 1.4 0.5 1.1 11.5

Composite EF Residual Marine 17.9 1.5 1.4 0.6 1.4 10.6

Note: a Estimated from PM10 using a multiplicative adjustment factor of 0.92.

The emission factors for auxiliary engines are shown in Table 3-53. The fuel specific main emission factors for NOX and HC were taken from several recent analyses of ship emissions in the U.S., Canada, and Europe, as referenced above for the main engine load factors. The PM factor for marine distillate was taken from a report by ENVIRON International, which


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was also referenced above. The PM factor for residual marine was based on discussions with the California Air Resources Board (ARB) staff. The CO factors are from the Starcrest Consulting study of the Port of Los Angeles.12 For SO2, the fuel specific emission factors were obtained from Entec and Corbett and Koehler.21,43

The composite emission factors displayed in the table are discussed below.

Table 3-53 Auxiliary Engine Emission Factors by Ship and Fuel Type Auxiliary Engine Emission Factors (g/kW-hr)

Engine Type Fuel Type NOX PM10 PM 2.5 a HC CO SO2

Medium Speed Marine Distillate 13.9 0.3 0.3 0.4 1.1 4.3

Medium Speed Residual Marine 14.7 1.5 1.4 0.4 1.1 12.3

Composite EF Residual Marine 14.5 1.2 1.1 0.4 1.1 **

Note: a Estimated from PM10 using a multiplicative adjustment factor of 0.92. b See Table 3-54 for composite SO2 emission factors by vessel type.

As for main engines, the STEEM study used the composite emission factors for auxiliary engines. For all pollutants other than SO2, underlying data used in the model do not explicitly identify auxiliary engine voyages by fuel type, i.e., marine distillate or residual marine. Again, the composite factor for those pollutants was determined by weighting individual emission factors by vessel engine population data from a 2005 survey of ocean-going vessels that was performed by ARB.20

For SO2, composite emission factors for auxiliary engines were calculated for each vessel type. These composite factors were determined by taking the fuel specific emission factors from Table 3-53 and weighting them with an estimate of the amount of marine distillate and residual marine that is used by these engines. The relative amount of each fuel type consumed was taken from the 2005 ARB survey. The relative amounts of each fuel type for each vessel type and the resulting SO2 emission factors are shown in Table 3-54.

Table 3-54 Auxiliary Engine SO2 Composite Emission Factors by Vessel Type

Vessel Type Residual Marine

(%) Marine Distillate

(%)

Composite Emission Factor

(g/kW-hr) Bulk Carrier 71 29 9.98 Container Ship 71 29 9.98 General Cargo 71 29 9.98 Passenger Ship 92 8 11.66 Refrigerated Cargo 71 29 9.98 Roll On-Roll Off 71 29 9.98 Tanker 71 29 9.98 Miscellaneous 0 100 4.3


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3.3.3.1.1.4 EPA Adjustments to STEEM PM and SO2 Emission Inventories

The interport emission results contained in this study for PM10 and SO2 were taken from the STEEM inventories and then adjusted to reflect EPA’s recent review of available engine test data and fuel sulfur levels as described Section 3.3.2.3 for the near port analysis. In the near ports work, a PM emission factor of 1.4 g/kW-hr was used for most main engines, e.g., slow speed diesel and medium speed diesel engines, all of which are assumed to use residual marine. A slightly higher value was used for steam turbine and gas turbine engines, and a slightly lower value was used for most auxiliary engines. However, these engines represent only a small fraction of the total emissions inventory. As shown in Section 3.3.3.1.1.3, the STEEM study used an emission factor of 1.5 g/kW-hr for all main engines and a slightly lower value for auxiliary engines. Here again, the auxiliary engines comprise only a small fraction of the total emissions from these ships. Therefore, for simplicity, EPA adjusted the interport PM inventories by multiplying the STEEM results by the ratio of the two primary emission factors, i.e., 1.4/1.5 or 0.933, to approximate the difference in fuel effects.

The STEEM SO2 emission inventories were similarly adjusted using SO2 emission factors from the interport analysis (Section 3.3.3.1.1.3) and the near ports analysis (Section 3.3.2.3). This information is displayed in Table 3-55. The composite values in the table are calculated by mathematically weighting the slow speed and medium speed emission factors from each study by their individual population fraction from the 2005 ARB shipping survey, i.e., 95 percent and 5 percent, respectively.20 Therefore, the interport SO2 inventories that appear in this report are the result of multiplying the STEEM inventories by the ratio of the two composite g/kW-hr emission factors shown in table, i.e., 10.33 /10.6 or 0.975.

Table 3-55 SO2 Emission Factors Used to Adjust STEEM Emission Inventories

Engine Type Fuel Type STEEM

(g/ kW -hr) Near Ports (g/ kW -hr)

Composite (g/ kW -hr)

Slow Speed Residual Marine 10.50 10.29 n/a

Medium Speed Residual Marine 11.50 11.09 n/a

Composite Residual Marine 10.6 a 10.33 a 0.975

Note: a Weighted by ship populations from 2005 ARB survey: 95 percent slow speed and 5 percent medium speed.

3.3.3.2 Interport Domestic Traffic

As previously noted, STEEM includes the emissions associated with ships that are engaged in foreign commerce. As a result, U.S.-flagged vessels carrying domestic cargo (Jones Act ships) are not included. The STEEM interport analysis also roughly estimated the emissions associated with these ships that are engaged solely in domestic commerce.1,4 Specifically, the interport analysis estimated that the large ocean-going vessels carrying only domestic cargo excluded from STEEM represent approximately 2-3 percent of the total U.S. emissions.

Deleted: . flag


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In Section 3.3.2.5, in the estimation of port inventories, the estimate of excluded installed

power was roughly 6.5 percent. It is not inconsistent that the STEEM estimate of excluded emissions is lower than the excluded power estimated from calls to U.S. ports, since the STEEM model includes ships that are transiting without stopping at U.S. ports. Since most of the Jones Act ships tend to travel closer along the coast line, most of the Jones Act ship traffic is expected to fall within the proposed ECA. Therefore, the results presented in this chapter are expected to underpredict the benefits of the proposed ECA.

3.3.3.3 Combining the Near Port and Interport Inventories

The national and regional inventories in this study are a combination of the results from the near ports analysis described in Section 3.3.2 and the STEEM interport modeling described in this section. The two inventories are quite different in form. As previously presented in Figure 3-1, the STEEM modeling domain spans the Atlantic and Pacific Oceans in the northern hemisphere. The model characterizes emissions from vessels while traveling between ports. That includes when a vessel is maneuvering a distance of 20 kilometers to enter or exist a port, cruising near a port as it traverses the area, or moving in a shipping lane across the open sea. For the U.S., STEEM includes the emissions associated with 251 ports. The results are spatially reported in a gridded format that is resolved to a cell dimension of 4 kilometers by 4 kilometers.

The near port results, however, are much more geographically limited and are not reported in a gridded format. The analysis includes the emissions associated with ship movements when entering or exiting each of 117 major U.S. ports. For deep sea ports that includes when a vessel is hotelling and maneuvering in the port, operating in the RSZ that varies in length for each port, and cruising 25 nautical miles between the end of the RSZ and an unconstrained shipping lane. For Great Lakes ports that includes hotelling and maneuvering, three nautical miles of RSZ operation, and cruising 7 nautical miles between the end of the RSZ and open water. The results are reported for each port and mode of operation.

To precisely replace only the portion of the STEEM interport inventory that is represented in the near port inventory results, it is necessary to spatially allocate the emissions in a format that is compatible with the STEEM 4 kilometers by 4 kilometers gridded output. Once that has been accomplished, the two inventories can be blended together. Both of these processes are described below. This work was conducted by ENVIRON International as a subcontractor under the EPA contract with ICF.2

3.3.3.3.1 Spatial Location of the Near Port Inventories

The hotelling, maneuvering, RSZ, and cruise emissions from the near port inventories were spatially located by their respective latitude and longitude coordinates using ArcGIS software. For this study, shapefiles were created that depicted the emission locations as described above. Additional shapefiles were also obtained to locate other geographic features such as the coastline and rivers of the U.S. These shapefiles and the STEEM output can be layered upon each other, viewed in ArcMap, and analyzed together. The following sections provide a more detailed description of how the shapefiles representing the ports, RSZ lanes, and cruise lanes were developed.


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3.3.3.3.1.1 Ports

Each port, and thus the designated location for hotelling and maneuvering emissions, is modeled as a single latitude/longitude coordinate point using the port center as defined by the Army Corp of Engineers in the Principal Ports of the United States dataset.11 One additional port, “Other Puget Sound,” which was specially created in the near ports analysis, was added to the list of ports. Some port locations were inspected by consulting Google Earth satellite images to ensure that the point that defined the port’s location was physically reasonable for the purposes of this analysis. This resulted in slightly modifying the locations of five ports: Gray’s Harbor, Washington; Freeport and Houston, Texas; Jacksonville, Florida; and Moreshead City, North Carolina. In all five cases the change was very small. The hotelling and maneuvering emissions represented by the latitude/longitude coordinate for each port were subsequently assigned to a single cell in the gridded inventory where that point was located. It should be noted that modeling a port as a point will over specify the location of the emissions associated with that port if it occupies an area greater than one grid cell, or 4 kilometers by 4 kilometers. The coordinates of all of the 117 ports used in this work are shown in the Appendix, Table 3-102.

3.3.3.3.1.2 Reduced Speed Zone Operation

The RSZ routes associated with each of the 117 ports were modeled as lines. Line shapefiles were constructed using the RSZ distance information described in Section 3.3.2 and the Army Corp of Engineers National Waterway Network (NWN) geographic database of navigable waterways in and around the U.S.16 The coordinates of RSZ endpoints for all of the 117 ports used in this work are shown in the Appendix, Table 3-103.

The RSZ emissions were distributed evenly along the length of the line. The latitude/longitude coordinates for each point along the line were subsequently used to assign the emissions to a grid cell based on the proportion of the line segment that occurred in the respective cell. Figure 3-4 illustrates how the length of the RSZ line can vary in any grid cell.

In several instances the NWN links and STEEM data indicated there were two RSZs. These ports are: Honolulu, Hawaii; Los Angeles, Long Beach and El Segundo, California; Brunswick, Georgia; and Baton Rouge, New Orleans, Port of South Louisiana, and Plaquemines, Louisiana. The lengths of the two lines were similar in every case, so the RSZ emissions from the near ports analysis were divided equally between both branches. Figure 3-5 shows an example of a port with multiple RSZs.


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Figure 3-4 Example of Gridded RSZ Lane (Hopewell, Virginia)


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Figure 3-5 Example of Multiple RSZ Lanes (Brunswick, Georgia)

3.3.3.3.1.3 Cruise Operations

The cruise mode links that extend 25 nautical miles for deep sea ports or 7 nautical miles for Great Lake ports from the end of the RSZ end point were also modeled with line shapefiles. These links were spatially described for each port following the direction of the shipping lane evident in the STEEM data. Again, as with RSZ emissions, the latitude/longitude coordinates for each point along the line were subsequently used to assign the emissions to a grid cell based on the proportion of the line segment that occurred in the respective cell.

The STEEM data sometimes indicated there were two or three cruise mode links associated with a port. In these cases, the underlying STEEM ship movement data was evaluated to determine whether any particular route should be assigned larger emissions than the others. That information was judged to be inadequate to justify such differential treatment, so the near port cruise emissions for ports with multiple cruise lanes were assigned equally to each link. Figure 3-6 provides an example of multiple cruise lanes.


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Figure 3-6 Example of Multiple Cruise Lanes (Tampa and Port Manatee, Florida)

3.3.3.3.2 Combining the Near Port and STEEM Emission Inventories

After spatially defining the geographic location of the near port emissions, but before actually inserting them into the gridded STEEM inventory, it was necessary to determine if all of the STEEM emissions within an affected cell should be replaced, or if some of the emissions should be retained. In this latter case, ships would be traversing the area near a port, but not actually entering or exiting the port.

This evaluation was performed for each port by first overlaying the RSZ and cruise shapefiles on the STEEM gridded inventory, and then using ArcGIS tools to create a series of circular buffers with a radius of 25 nautical miles around each of the points that represented an RSZ line. A single elongated buffer was then made from the intersection or outer boundaries of all the individual circular buffers. As illustrated in Figure 3-6, the resulting RSZ buffer encloses the port, RSZ links and cruise mode links. The STEEM emissions underneath the buffer were then evaluated. In cases where the STEEM data showed that ships were routed directly to an isolated port, the STEEM emissions were completely replaced by near port emissions (Figure 3-7). Conversely, when the examination revealed that the underlying STEEM emissions included some ship passages that were simply traversing near the port, the emissions associated with those vessel movements were retained, i.e., not completely replaced with the near port


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emission results (Figure 3-8). The methodology for determining the emissions from transient ship operation is described below.

Figure 3-7 Example of Complete Replacement of STEEM Emissions (Panama City, Florida)


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Figure 3-8 Example of Partial Replacement of STEEM Emissions (Coos Bay, Oregon)

The percentage of STEEM emissions that are attributable to a port, and should be replaced, were approximated by dividing the STEEM emissions in the isolated portion of the route that lead only to the port, with the STEEM emissions in the major shipping lane. As an example, the STEEM emissions in the portion of the buffer in Figure 3-8 that only went to the port were approximately 347 kg/cell/year. The emissions within the buffer for just the major shipping lane were 6996 kg/cell/year. Therefore, the emissions in the grid cells that comprised the portion of the buffer overlaying the major shipping lane were reduced by the fraction 347/6996, or 5 percent before the near port emissions were added to the gridded inventory.

The actual merging of the two inventories was performed by creating a number of databases that identified the fraction of the near port inventory for each pollutant species and operating mode that should be added to the grid cells for each port. A similar database was also created that identified how much of the original STEEM emissions should be reduced to account for ship movements associated directly with a port, while preserving those that represented transient vessel traffic. These databases were subsequently used to calculate the new emission results for each affected cell in the original STEEM gridded inventory, resulting in the combined inventory results for this study.


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Figure 3-9 provides side-by-side comparisons of the original STEEM emissions inventory and the new merged inventory. The results indicate that the spatial allocation of the near port emissions conducted in this study provides a more precise assessment of vessel travel near a port than the STEEM methodology. As previously described, the near port ship emissions may be over specified, but this approach generally provides a more reasonable placement of emissions near the coastline than the wide shipping lanes in the STEEM model, which in some cases show shipping emissions over land.


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Original

New

Figure 3-9 Spatial Comparison of the Original STEEM and New Combined Gridded Inventories—Southeast United States


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3.3.4 2002 Baseline Emission Inventories

The modeling domain of the new combined emission inventory described above is the same as the original STEEM domain, i.e., the Atlantic and Pacific Oceans, the Gulf of Mexico, the Great Lakes, Alaska and Hawaii. Inventories for the nine geographic regions of the U.S. specified in Section 3.2 were created using ArcGIS software to intersect the regional shapefiles with the 4 kilometers by 4 kilometers gridded domain. Any grid cell split by a regional boundary was considered to be within a region if over 50 percent of its area was within the region. The emissions in each of the cells defined within a region were then summed. The final emission inventories for 2002 are shown in Table 3-56 for each of the nine geographic regions and the nation. The geographic scope of these regions was previously displayed in Figure 3-1. The fuel consumption by fuel type associated with each region is also provided in Table 3-57.

Table 3-56 2002 Regional and National Emissions from Category 3 Vessel Main and Auxiliary Engines Metric Tonnes

Region NOX PM10 PM2.5 a HC CO SO2 CO2

Alaska East (AE) 18,051 1,425 1,311 597 1,410 10,618 657,647 Alaska West (AW) 60,019 4,689 4,313 1,989 4,685 34,786 2,143,720 East Coast (EC) 219,560 17,501 16,101 7,277 17,231 145,024 8,131,553 Gulf Coast (GC) 172,897 14,043 12,920 5,757 14,169 104,852 6,342,139 Hawaii East (HE) 22,600 1,775 1,633 749 1,765 13,182 818,571 Hawaii West (HW) 31,799 2,498 2,297 1,053 2,484 18,546 1,151,725 North Pacific (NP) 26,037 2,154 1,982 938 2,090 15,295 990,342 South Pacific (SP) 104,155 8,094 7,447 3,464 8,437 60,443 3,796,572 Great Lakes (GL) 15,019 1,179 1,085 498 1,174 8,766 541,336 Total Metric Tonnes 670,137 53,358 49,089 22,322 53,445 411,512 24,573,605 Total Short Tons b 738,700 58,817 54,112 24,606 58,913 453,614 27,087,763 Notes: a Estimated from PM10 using a multiplicative adjustment factor of 0.92. b Converted from metric tonnes using a multiplicative conversion factor of 1.102 short tons per metric tonne.

Table 3-57 2002 Regional and National Fuel Consumption Metric Tonnes Fuel

Region Distillate Residual Total Alaska East (AE) 1,887 204,725 206,612Alaska West (AW) 0 673,490 673,490East Coast (EC) 91,529 2,463,153 2,554,682Gulf Coast (GC) 63,876 1,928,628 1,992,504Hawaii East (HE) 4,375 252,794 257,170Hawaii West (HW) 0 361,836 361,836North Pacific (NP) 15,905 295,230 311,135South Pacific (SP) 35,052 1,157,714 1,192,765Great Lakes (GL) 1,270 168,801 170,071Total Metric Tonnes 213,894 7,506,371 7,720,265Total Short Tons b 235,778 8,274,358 8,510,136


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As previously noted, the inventories in the above table reflect the emissions associated

with Category 3 ocean-going vessels that are engaged in foreign commerce. The STEEM interport analysis also roughly estimated the emissions associated with these ships that are engaged solely in domestic commerce.1,4 These vessels are sometimes referred to as Jones Act ships, as explained in Section 3.3.2.5. Specifically, the interport analysis estimated that the emissions from large ocean-going vessels carrying only domestic cargo represent approximately 2-3 percent of the total values presented in Table 3-56. This is less than the 6.5 percent estimate based on calls to U.S. ports, since the interport traffic includes transiting traffic in U.S. waters.

The relative contributions of the near port and interport emission inventories to the total U.S. emissions are presented in Table 3-58 and Table 3-59. As expected, based on the geographic scope of the two types of inventories, the interport and near port inventories are about 80 percent and 20 percent of the total, respectively. The deep sea ports are about 97 to nearly 100 percent and the Great Lake ports are about 3 to almost zero percent of the total inventories, depending on the port region. This result is also expected given the small number of Great Lake ports and more limited geographic area of the modeling domain.

Table 3-58 2002 Contribution of Near Ports and Interport Emissions to the Total C3 Inventory Metric Tonnes

NOX PM10 PM2.5 a

Region and Port Type Total

% Region

% Type Total

% Region

% Type Total

% Region

% Type

549,852 82.1 100 42,945 80.5 100 39,510 80.5 100 535,325 -- 97.4 41,811 -- 97.4 38,465 -- 97.4

Interport Deep Sea Great Lakes 14,528 -- 2.6 1,135 -- 2.6 1,044 -- 2.6

120,285 17.9 100 10,413 19.5 100 9,580 19.5 100 119,793 -- 99.6 10,368 -- 99.6 9,539 -- 99.6

Near Port Deep Sea Great Lakes 491 -- 0.4 44 -- 0.4 41 -- 0.4

670,137 100 -- 53,358 100 -- 49,089 100 -- 655,118 -- 97.8 52,179 -- 97.8 48,004 -- 97.8

All Regions Deep Sea Great Lakes 15,019 -- 2.2 1,179 -- 2.2 1,085 -- 2.2 All Region Short Tons b 738,700 -- -- 58,817 -- -- 54,112 -- --

Notes: a Estimated from PM10 using a multiplicative adjustment factor of 0.92. b Converted from metric tonnes using a multiplicative adjustment factor of 1.102 short tons per metric tonne.


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Table 3-59 2002 Contribution of Near Ports and Interport Emissions to the Total C3 Inventory (Cont’d)

Metric Tonnes HC CO SO2

Region and Port Type Total

% Region

% Type Total

% Region

% Type Total

% Region

% Type

18,219 81.6 100 42,912 80.3 100 318,450 77.4 100 17,738 -- 97.4 41,778 -- 97.4 310,030 -- 97.4

Interport Deep Sea Great Lakes 481 -- 2.6 1,134 -- 2.6 8,420 -- 2.6

4,103 18.4 100 10,533 19.7 100 93,062 22.6 100 4,086 -- 99.6 10,493 -- 99.6 92,716 -- 99.6

Near Port Deep Sea Great Lakes 17 -- 0.4 40 -- 0.4 346 -- 0.4

22,322 100 -- 53,445 100 -- 411,512 100 -- 21,824 -- 97.8 52,271 -- 97.8 402,746 -- 97.9

All Regions Deep Sea Great Lakes 498 -- 2.2 1,174 -- 2.2 8,766 -- 2.1 All Region Short Tons a 24,606 -- -- 58,913 -- -- 453,614 -- --

Note: a Converted from metric tonnes using a multiplicative adjustment factor of 1.102 short tons per metric tonne.

3.4 Development of 2020 and 2030 Scenarios

3.4.1 Outline of Methodology

The emissions from Category 3 ocean-going vessels (main propulsion and auxiliary engines) are projected to 2020 and 2030 by applying certain adjustment factors to the 2002 emission inventories to account for the change in ship traffic over these time periods, i.e., growth, and the effect of the current controls and the NOX and fuel controls described in the proposed rule.

The following sections describe the derivation of the growth adjustment factors for each of the modeling regions described in Section 3.2, the emission adjustment factors, and the resulting 2020 and 2030 emission inventories.

The final section describes the baseline and control emission inventories that were developed for calendar years 2020 and 2030. The 2030 inventories were used for air quality modeling, although the 2020 control inventories reported here have been updated relative to those used for the air quality modeling. A comparison of the 2020 control case inventories reported here with those used for the air quality modeling is provided in Section 3.7.

3.4.2 Growth Factors by Geographic Region

This section describes the growth factors that are used to project the emissions to 2020 and 2030 for each of the nine geographic regions evaluated in this analysis. These factors are based on the expected demand for marine bunker fuels that is associated with shipping goods, i.e., commodities, into and out of the U.S. The use of bunker fuel as a surrogate for estimating future emissions is appropriate because the quantity of fuel consumed by C3 engines is highly


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correlated with the amount of combustion products, i.e., pollutants, that are emitted from those vessels. The term bunker fuel in this report also includes marine distillate oil and marine gas oil that are used in some auxiliary power engines.

The remainder of this section first summarizes the development of growth rates by RTI International (RTI) for five geographic regions of the U.S., as performed under contract to EPA (Section 3.4.2.1).5,6 This is followed by the derivation of the growth factors that are used in this study for the nine geographic regions of interest (Section 3.4.2.9).

3.4.2.1 Summary of Regional Growth Rate Development

RTI developed fuel consumption growth rates for five geographic regions of the U.S. These regions are the East Coast, Gulf Coast, North Pacific, South Pacific, and Great Lakes. The amount of bunker fuel required in any region and year is based on the demand for transporting various types of cargo by Category 3 vessels. This transportation demand is in turn driven by the demand for commodities that are produced in one location and consumed in another, as predicted by an econometric model. The flow of commodities is matched with typical vessels for trade routes (characterized according to cargo capacity, engine horsepower, age, specific fuel consumption, and engine load factors). Typical voyage parameters are then assigned to the trade routes that include average ship speed, round trip mileage, tons of cargo shipped, and days in port. Fuel consumption for each trade route and commodity type thus depends on commodity projections, ship characteristics, and voyage characteristics. Figure 3-10 from RTI illustrates the approach to developing baseline projections of marine fuel consumption.

As a means of comparison, the IMO Secretary General’s Informal Cross Government/Industry Scientific Group of Experts presented a growth rate that ranged from 3.3% to 3.7%.46 RTI’s overall U.S. growth rate was projected at 3.4%, which is consistent with that range.


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Figure 3-10 Illustration of Method for Estimating Bunker Fuel Demand

3.4.2.2 Trade Analysis

The trade flows between geographic regions of the world, as illustrated by the middle portion of Figure 3-10, were defined for the following eight general types of commodities: - liquid bulk – crude oil - liquid bulk – refined petroleum products

- liquid bulk – residual petroleum products


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- liquid bulk – chemicals (organic and inorganic) - liquid bulk –gas (including LNG and LPG) - dry bulk (e.g., grain, coal, steel, ores and scrap) - general cargo (e.g., lumber/forest products) - containerized cargo

The analysis specifically evaluated trade flows between 21 regions of the world. Table

3-60 shows the countries associated with each region.

Table 3-60 Aggregate Regions and Associated Countries Aggregate Regions Base Countries / Regions

U.S. Atlantic Coast U.S. Atlantic Coast U.S. Great Lakes U.S. Great Lakes U.S. Gulf Coast U.S. Gulf Coast E. Canadaa Canadaa W. Canadaa Canadaa U.S. Pacific North U.S. Pacific North U.S. Pacific South U.S. Pacific South Greater Caribbean Colombia, Mexico, Venezuela, Caribbean Basin, Central America

South America Argentina, Brazil, Chile, Peru, Other East Coast of S. America, Other West Coast of S. America

Africa – West Western Africa Africa-North/East-Mediterranean Mediterranean Northern Africa, Egypt, Israel, Africa-East/South Kenya, Other Eastern Africa, South Africa, Other Southern Africa

Europe-North Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Netherlands, Norway, Sweden, Switzerland, United Kingdom

Europe-South Greece, Italy, Portugal, Spain, Turkey, Other Europe Europe-East Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic Caspian Region Southeast CIS Russia/FSU The Baltic States, Russia Federation, Other Western CIS Middle East Gulf Jordan, Saudi Arabia, UAE, Other Persian Gulf Australia/NZ Australia, New Zealand Japan Japan

Pacific-High Growth Hong Kong S.A.R., Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand

China China Rest of Asia Viet Nam, India, Pakistan, Other Indian Subcontinent

Note: a Canada is treated as a single destination in the GI model. Shares of Canadian imports from and exports to regions of the world in 2004 are used to divide Canada trade into shipments to/from Eastern Canada ports and shipments to/from Western Canada ports.47


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The overall forecast of demand for shipping services and bunker fuel was determined for each of the areas using information on commodity flows from Global Insight’s (GI) World Trade Service. Specifically, GI provided a specialized forecast that reports the flow of each commodity type for the period 1995–2024, based on a proprietary econometric model. The general structure of the GI model for calculating trade flows assumes a country’s imports from another country are driven by the importing country’s demand forces (given that the exporting country possesses enough supply capacity), and affected by exporting the country’s export price and importing country’s import cost for the commodity. The model then estimates demand forces, country-specific exporting capacities, export prices, and import costs.

The GI model included detailed annual region-to-region trade flows for eight composite commodities from 1995 to 2024, in addition to the total trade represented by the commodities. Table 3-61 illustrates the projections for 2012 and 2020, along with baseline data for 2005. In 2005, dry bulk accounted for 41 percent of the total trade volume, crude oil accounted for 28 percent, and containers accounted for 12 percent. Dry bulk and crude oil shipments are expected to grow more slowly over the forecast period than container shipments. By 2020, dry bulk represents 39 percent of the total, crude oil is 26 percent, and containers rise to 17 percent.

Table 3-61 Illustration of World Trade Estimates for Composite Commodities, 2005, 2012, and 2020 Cargo (millions of tons)

Commodity Type 2005 2012 2020 Dry Bulk 2,473 3,051 3,453Crude Oil 1,703 2,011 2,243Container 714 1,048 1,517Refined Petroleum 416 471 510General Cargo 281 363 452Residual Petroleum and Other Liquids 190 213 223Chemicals 122 175 228Natural Gas 79 91 105Total International Cargo Demand 5,979 7,426 8,737

3.4.2.3 Ship Analysis by Vessel Type and Size

Different types of vessels are required to transport the different commodities to the various regions of the world. As shown at the top of Figure 3-10, profiles of these ships were developed to identify the various vessel types and size categories that are assigned to transport commodities of each type along each route. These profiles include attributes such as ship size, engine horsepower, engine load factors, age, and engine fuel efficiency. This information was subsequently used to estimate average daily fuel consumption for each typical ship type and size category.


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The eight GI commodity categories were mapped to the type of vessel that would be used to transport that type of cargo using information from Clarksons Shipping Database.48 These assignments are shown in Table 3-62.

Table 3-62 Assignment of Commodities to Vessel Types

Commodity Ship Category Vessel Type

Liquid bulk – crude oil Crude Oil Tankers Tanker

Liquid bulk – refined petroleum products

Product Tankers Product Carrier

Liquid bulk – residual petroleum products

Product Tankers Product Carrier

Liquid bulk – chemicals (organic and inorganic)

Chemical Tankers Chemical & Oil Carrier

Liquid bulk – natural gas (including LNG and LPG)

Gas Carriers LNG Carrier, LPG Carrier, Chemical & LPG Carrier, Ethylene/LPG, Ethylene/LPG/Chemical, LNG/Ethylene/LPG, LNG/Regasification, LPG/Chemical, LPG/Oil, Oil & Liquid Gas Carrier

Dry bulk (e.g. grain, coal, steel, ores and scrap)

Dry Bulk Carriers Bulk Carrier

General cargo (including neobulk, lumber/forest products)

General Cargo General Cargo Liner, Reefer, General Cargo Tramp, Reefer Fish Carrier, Ro-Ro, Reefer/Container, Ro-Ro Freight/Passenger, Reefer/Fleet Replen., Ro-Ro/Container, Reefer/General Cargo, Ro-Ro/Lo-Lo, Reefer/Pallets Carrier, Reefer/Pass./Ro-Ro, Reefer/Ro-Ro Cargo

Containerizable cargo Container Ships Fully Cellular Container

Each of the vessel types were classified by their cargo carrying capacity or deadweight tons (DWT). The size categories were identified based on both industry definitions and natural size breaks within the data. Table 3-63 summarizes the size categories that were used in the analysis and provides other information on the general attributes of the vessels from Clarksons Shipping Database. The vessel size descriptions are also used to define shipping routes based on physical limitations that are represented by canals or straits through which ships can pass. Very large crude oil tankers are the largest by DWT rating, and the biggest container ships (Suezmax) are also very large.


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Table 3-63 Fleet Characteristics

Ship Type Size by DWT

Minimum Size

(DWT)

Maximum Size

(DWT) Number of Ships

Total DWT

(millions)

Total Horse Power

(millions)

Suezmax 83,000 140,000 101 9.83 8.56 PostPanamax 56,500 83,000 465 30.96 29.30 Panamax 42,100 56,500 375 18.04 15.04 Intermediate 14,000 42,100 1,507 39.80 32.38

Container

Feeder 0 14,000 1,100 8.84 7.91 General Cargo All All 3,214 26.65 27.07

Capesize 79,000 0 715 114.22 13.81 Panamax 54,000 79,000 1,287 90.17 16.71 Handymax 40,000 54,000 991 46.50 10.69

Dry Bulk

Handy 0 40,000 2,155 58.09 19.58 VLCC 180,000 0 470 136.75 15.29 Suezmax 120,000 180,000 268 40.63 5.82 AFRAmax 75,000 120,000 511 51.83 8.58 Panamax 43,000 75,000 164 10.32 2.17 Handymax 27,000 43,000 100 3.45 1.13

Crude Oil Tanker

Coastal 0 27,000 377 3.85 1.98 Chemical Tanker All All 2,391 38.80 15.54

AFRAmax 68,000 0 226 19.94 3.60 Panamax 40,000 68,000 352 16.92 4.19 Handy 27,000 40,000 236 7.90 2.56

Petroleum Product Tanker

Coastal 0 27,000 349 3.15 1.54 VLGC 60,000 0 157 11.57 5.63 LGC 35,000 60,000 140 6.88 2.55

Natural Gas Carrier

Midsize 0 35,000 863 4.79 3.74

Other All All 7,675 88.51 53.60

Total -- -- -- 26,189 888.40 308.96

The average fuel consumption for each vessel type and size category was estimated in a multi-step process using individual vessel data on engine characteristics. Clarksons’ Shipping Database Register provides each ship’s total installed horsepower (HP), type of propulsion (diesel or steam), and year of build. These characteristics are then matched to information on typical specific fuel consumption (SFC), which is expressed in terms of grams of bunker fuel burned per horsepower-hour (g/HP-hr).


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0

20

40

60

80

100

120

140

160

180

200

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Spec

ific

Fuel

Con

sum

ptio

n (g

/bhp

-hou

r) The specific SFC values are based on historical data from Wartsila Sulzer, a popular

manufacturer of diesel engines for marine vessels. RTI added an additional 10 percent to the reported “test bed” or “catalogue” numbers to account for the guaranteed tolerance level and an in-service SFC differential. Overall, the 10 percent estimate is consistent with other analyses that show some variation between the “test bed” SFC values reported in the manufacturer product catalogues and those observed in actual service. This difference is explained by the fact that old, used engines consume more fuel than brand new engines and in-service fuels may be different than the test bed fuels.49

Figure 3-11 shows SFC values that were used in the model regarding the evolution of specific fuel oil consumption rates for diesel engines over time. Engine efficiency in terms of SFC has improved over time, most noticeably in the early 1980s in response to rising fuel prices. However, there is a tradeoff between improving fuel efficiency and reducing emissions. Conversations with engine manufacturers indicate that it is reasonable to assume SFC will remain constant for the projection period of this study, particularly as they focus on meeting NOX emission standard as required by MARPOL Annex VI, or other potential pollution control requirements.

Figure 3-11 Diesel Engine Specific Fuel Consumption

RTI assumed a fixed SFC of 220 g/HP-hr for steam engines operating on bunker fuel.

Using the above information, the average daily fuel consumption (AFC), expressed in metric tons of fuel at full engine load, for each vessel type and size category is found using the following equation:


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Equation 3-27

[ ]∑ −××= gtonnesHPSFCN svsvsv /101AFCFleet 6

,,,

Where: Fleet AFC = Average daily fuel consumption in metric tonnes at full engine load v = Vessel type s = Vessel size category N = Number of vessels in the fleet

SFC = Specific fuel consumption in grams of bunker fuel burned per horsepower-hour in use(g/HP-hr) HP = Total installed engine power, in horsepower (HP) 106 tonnes/g = Conversion from grams to metric tonnes

As previously noted, AFC values calculated in the above equation are based on total

horsepower; therefore, they must be scaled down to reflect typical operation using less than 100 percent of the horsepower rating, i.e., actual engine load. Table 3-64 shows the engine load factors that were used to estimate the typical average daily fuel consumption (tons/day) for the main propulsion engine and the auxiliary engines when operated at sea and in port.50

Table 3-64 Main and Auxiliary Engine Load Factors

Vessel Type

Main Engine

Load Factor (%)

Auxiliary Engine as Percent of Main

Engine

Auxiliary Engine as Percent of Main Engine at

Sea Container Vessels 80 22.0 11.0 General Cargo Carriers 80 19.1 9.5 Dry Bulk Carriers 75 22.2 11.1 Crude Oil Tankers 75 21.1 10.6 Chemical Tankers 75 21.1 10.6 Petroleum Product Tankers 75 21.1 10.6 Natural Gas Carrier 75 21.1 10.6 Other 70 20.0 10.0

The RTI analysis also assumed that the shipping fleet changes over time as older vessels

are scrapped and replaced with newer ships. Specifically, vessels over 25 years of age are retired and replaced by new ships of the most up-to-date configuration. This assumption leads to the following change in fleet characteristics over the projection period:

• New ships have engines rated at the current SFC, so even though there are no further improvements in specific fuel consumption, the fuel efficiency of the fleet as a whole will improve over time through retirement and replacement.


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• New ships will weigh as much as the average ship built in 2005, so the total cargo capacity of the fleet will increase over time as smaller ships retire and are replaced.

• Container ships will increase in size over time on the trade routes between Asia to

either North America or Europe.

3.4.2.4 Trade Analysis by Commodity Type and Trade Route

Determining the total number of days at sea and in port, as shown in the middle portion of Figure 3-10, requires information on the relative amount of each commodity that is carried by the different ship type size categories on each of the trade routes. For example, to serve the large crude oil trade from the Middle East Gulf region to the Gulf Coast of the U.S., 98 percent of the deadweight tonnage is carried on very large oil tankers, while the remaining 2 percent is carried on smaller Suezmax vessels. After the vessel type size distribution was found, voyage parameters were estimated. Specifically, these are days at sea and in port for each voyage (based on ports called, distance between ports, and ship speed), and the number of voyages (based on cargo volume projected by GI and the DTW from Clarksons Shipping Database). The length of each voyage and number of voyages were used to estimate the total number of days at sea and at port, which is a parameter used later to calculate total fuel consumption for each vessel type and size category over each route and for each commodity type. (More information on determining the round trip distance for each voyage that is associated with cargo demand for the U.S. is provided in Section 3.4.2.5.)

The days at sea were calculated by dividing the round trip distance by the average vessel speed:

Equation 3-28

hrs 24speed route distance triproundVoyagePer Seaat Days

,,, ×

=sv

routesv

Where: v = Vessel type s = Vessel size category route = Unique trip itinerary round trip route distance = Trip length in nautical miles speed = Vessel speed in knots or nautical miles per hour 24 hrs = Number of hours in one day

Table 3-65 presents the speeds by vessel type that were used in the analysis.50 These

values are the same for all size categories, and are assumed to remain constant over the forecast period.


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Table 3-65 Vessel Speed by Type

Vessel Type Speed (knots) Crude Oil Tankers 13.2 Petroleum Product Tankers 13.2 Chemical Tankers 13.2 Natural Gas Carriers 13.2 Dry Bulk Carriers 14.1 General Cargo Vessels 12.3 Container Vessels 19.9 Other 12.7

The number of voyages along each route for each trade was estimated for each vessel

type v and size category s serving a given route by dividing the tons of cargo moved by the amount of cargo (DTW) per voyage:

Equation 3-29

raten utilizatioDWT averagefleet moved cargo of tonnesmetric totalVoyages ofNumber

,,, ×

=sv

tradesv

Where: v = Vessel type s = Vessel size category trade = Commodity type Fleet average DWT = Median dead weight tonnage carrying capacity in metric tons Utilization rate = Fraction of total ship DWT capacity used

The cargo per voyage is based on the fleet average ship size from the vessel profile

analysis. For most cargo, a utilization rate of 0.9 is assumed to be constant throughout the forecast period. Lowering this factor would increase the estimated number of voyages required to move the forecasted cargo volumes, which would lead to an increase in estimated fuel demand.

In addition to calculating the average days at sea per voyage, the average days in port per voyage was also estimated by assuming that most types of cargo vessels spend four days in port per voyage. RTI notes, however, that this can vary somewhat by commodity and port.

3.4.2.5 Worldwide Estimates of Fuel Demand

This section describes how the information from the vessel and trade analyses were used to calculate the total annual fuel demand associated with international cargo trade. Specifically, for each year y of the analysis, the total bunker fuel demand is the sum of the fuel consumed on each route of each trade (commodity). The fuel consumed on each route of each trade is in turn the sum of the fuel consumed for each route and trade for that year by propulsion main engines


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and auxiliary engines when operated at sea and in port. These steps are illustrated by the following equations:

Equation 3-30

y trade,route,year

trade route

trade,route,yatsea trade,route,y trade,route,yat port trade,route,ytrade route

FC FC

AFC x DaysatSea AFC x Daysat Port

= Σ Σ

⎡ ⎤= Σ Σ +⎣ ⎦

Where: FC = Fuel consumed in metric tonnes y = calendar year trade = Commodity type route = Unique trip itinerary AFC = Average daily fuel consumption in metric tonnes yatsea = Calendar year main and auxiliary engines are operated at sea yatport = Calendar year main and auxiliary engines are operated in port

Equations 3-31

( )trade,route,yat sea v,s v,sv,s,t,r

trade,route,yat port v,s v,sv,s,t,r

trade,route,y v

AFC (Percent of tradealong route) Fleet AFC x MELF AEat sea LF

AFC (Percent of tradealong route) Fleet AFC x AEimport LF

DaysatSea

⎡ ⎤= Σ +⎣ ⎦

⎡ ⎤= Σ ⎣ ⎦

= Σ

[ ]v,s v,s v,s,s,t,r

trade,route,y v,sv,s,t,r

(Percent of tradealong route) Daysat sea per voyage x Number of voyages

Daysat Port (Percent of tradealong route) Daysat port per voyage x Number of voyages

⎡ ⎤⎣ ⎦

= Σ

Where: AFC = Average daily fuel consumption in metric tonnes trade = Commodity type route = Unique trip itinerary yatsea = Calendar year main and auxiliary engines are operated at sea yatport = Calendar year main and auxiliary engines are operated in port y = calendar year

v = Vessel type s = Vessel size category t = Trade r = Route Fleet AFC = Average daily fuel consumption in metric tonnes at full engine load MELF = main engine load factor, unitless AE at sea LF = auxiliary engine at-sea load factor, unitless AE in port LF = auxiliary engine in-port load factor, unitless

The inputs for these last four equations are all derived from the vessel analysis in Section 3.4.2.3 and the trade analysis in Section 3.4.2.2.


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3.4.2.6 Worldwide Bunker Fuel Consumption

Based on the methodology outlined above, estimates of global fuel consumption over time were computed, and growth rates determined from these projections. Figure 3-12 shows estimated world-wide bunker fuel consumption by vessel type.

0

100

200

300

400

500

600

1995

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2005

2010

2015

2020

Mill

ion

Ton

s of F

uel

Container General Cargo Dry Bulk Crude OilChemicals Petroleum Natural Gas OtherFishing Vessels Passenger Ships Military Vessels

Figure 3-12 Worldwide Bunker Fuel Consumption

Figure 3-13 shows the annual growth rates by vessel-type/cargo that are used in the projections shown in Figure 3-12. Total annual growth is generally between 2.5 percent and 3.5 percent over the time period between 2006 and 2020 and generally declines over time, resulting in an average annual growth of around 2.6 percent.


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-2%

0%

2%

4%

6%

8%

10%

2006

2008

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Perc

ent C

hang

e in

Bun

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Fuel

Total Container General Cargo Dry BulkCrude Oil Chemicals Petroleum Natural GasOther Fishing Vessels Passenger Ships Military Vessels

Figure 3-13 Annual Growth Rate in World-Wide Bunker Fuel Use by Commodity Type

3.4.2.7 Fuel Demand Used to Import and Export Cargo for the United States

The methodology described above provides an estimate of fuel consumption for international cargo worldwide. RTI also estimated the subset of fuel demand for cargo imported to and exported from five regions of the U.S. The five regions are:

• North Pacific • South Pacific • Gulf • East Coast • Great Lakes

For this analysis, the same equations were used, but were limited to routes that carried

cargo between specific cities in Asia, Europe and Middle East to the various ports in the specific regions of the U.S.

The trip distances for non-container vessel types were developed from information from Worldscale Association and Maritime Chain.51 The data from Worldscale is considered to be the


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industry standard for measuring port-to-port distances, particularly for tanker traffic. The reported distances account for common routes through channels, canals, or straits. This distance information was supplemented by data from Maritime Chain, a web service that provides port-to-port distances along with some information about which channels, canals, or straits must be passed on the voyage.

Voyage distances for container vessels are based on information from Containerization International Yearbook (CIY) and calculations by RTI. That reference provides voyage information for all major container services. Based on the frequency of the service, number of vessels assigned to that service, and the number of days in operation per year, RTI estimated the average length of voyages for the particular bilateral trade routes in the Global Insights trade forecasts.

The distance information developed above was combined with the vessel speeds previously shown in Table 3-65 to find the length of a voyage in days. Table 3-66 presents the day lengths for non-containerized vessel types and Table 3-67 shows the same information for container vessels.

Table 3-66 Day Length for Voyages for Non-Container Cargo Ship (approximate average)

Days per Voyage

Global Insights Trade Regions US South

Pacific US North

Pacific US East Coast

US Great Lakes

US Gulf

Africa East-South 68 75 57 62 54 Africa North-Mediterranean 49 56 37 43 47 Africa West 56 63 36 46 43 Australia-New Zealand 48 47 65 81 63 Canada East 37 46 7 18 19 Canada West 11 5 40 58 39 Caspian Region 95 89 41 46 48 China 41 36 73 87 69 Europe Eastern 61 68 38 45 46 Europe Western-North 53 60 24 32 34 Europe Western-South 54 61 30 37 37 Greater Caribbean 26 33 16 29 17 Japan 35 31 65 81 62 Middle East Gulf 77 72 56 65 83 Pacific High Growth 52 48 67 76 88 Rest of Asia 68 64 66 64 73 Russia-FSU 64 71 38 46 48 Rest of South America 51 30 41 46 44


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Table 3-67 Day Length for Voyages for Container-Ship Trade Routes

Origin – Destination Regions Days per Voyage

Asia – North America (Pacific) 37

Europe – North America (Atlantic) 37

Mediterranean – North America 41

Australia/New Zealand – North America 61

South America – North America 48

Africa South – North America (Atlantic) 54

Africa West – North America (Atlantic) 43

Asia – North America (Atlantic) 68

Europe – North America (Pacific) 64

Africa South – North America (Pacific) 68

Africa West – North America (Pacific) 38

Caspian Region – North America (Atlantic) 42

Caspian Region – North America (Pacific) 38

Middle East/Gulf Region – North America (Atlantic) 63

Middle East/Gulf Region – North America (Pacific) 80

3.4.2.8 Bunker Fuel Consumption for the United States

Figure 3-14 and Figure 3-15 present the estimates of fuel use for delivering trade goods to and from the U.S. The results in Figure 3-14 show estimated historical bunker fuel use in year 2001 of around 47 million tons (note: while this fuel is used to carry trade goods to and from the U.S., it is not necessarily all purchased in the U.S. and is not all burned in U.S. waters). This amount grows to over 90 million tons by 2020 with the most growth occurring on trade routes from the East Coast and the “South Pacific” region of the West Coast.


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0

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20

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40

50

60

70

80

90

100

1995

2000

2005

2010

2015

2020

Mill

ion

Ton

s of B

unke

r Fu

el

US North Pacific US Great Lakes US Gulf US East Coast US South Pacific

Figure 3-14 Bunker Fuel Used to Import and Export Cargo by Region of the United States

Figure 3-15 shows the estimated annual growth rates for the fuel consumption that are used in the projections shown in Figure 3-14. Overall, the average annual growth rate in marine bunkers associated with future U.S. trade flows is 3.4 percent between 2005 and 2020.


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-2%

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4%

6%

8%

10%

2006

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Perc

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Bun

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Fuel

United States US South Pacific US North Pacific

US Great Lakes US Gulf US East Coast

Figure 3-15 Annual Growth Rates for Bunker Fuel Used to Import and Export Cargo by Region of the United States

3.4.2.9 2020 and 2030 Growth Factors for Nine Geographic Regions

The results of the RTI analysis described above are used to develop the growth factors that are necessary to project the 2002 base year emissions inventory to 2020 and 2030. The next two sections describe how the five RTI regions were associated with the nine regions analyzed in this report, and how the specific growth rates for each of the nine regions were developed.

3.4.2.9.1 Mapping the RTI Regional Results to the Nine Region Analysis

As described in Section 3.3.4, the nine geographic regions analyzed in this study were designed to be consistent with the five RTI regional modeling domains. More specifically, four of the nine geographic areas in this study, i.e., Alaska East, Alaska West, Hawaii East, and Hawaii West are actually subsets of two broader regional areas that were analyzed by RTI, i.e., the North Pacific for both Alaska regions and South Pacific for Hawaii. Therefore, the growth rate information from the related larger region was assumed to be representative for that state.

The nine geographic regions represented in the emission inventory study are presented in Figure 3-1. The association of the RTI regions to the emission inventory regions is shown in Table 3-68.


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Table 3-68 Association of the RTI Regions to the Nine Emission Inventory Regions

Consumption Region Corresponding Emission

Inventory Region

North Pacific North Pacific (NP) North Pacific Alaska East (AE) North Pacific Alaska West (AW) South Pacific South Pacific (SP) South Pacific Hawaii East (HE) South Pacific Hawaii West (HW) Gulf Gulf Coast (GC) East Coast East Coast (EC) Great Lakes Great Lakes (GL)

3.4.2.9.2 Growth Factors for the Emission Inventory Analysis

Emission inventories for 2020 and 2030 are estimated in Section 3.4.5 by multiplying the 2002 baseline inventory for each region by a corresponding growth factor that was developed from the RTI regional results. Specifically, the average annual growth rate from 2002-2020 was calculated for each of the five regions. Each regional growth rate was then compounded over the inventory projection time period for 2020 and 2030, i.e., 18 and 28 years, respectively. The resulting multiplicative growth factors for each emission inventory region and the associated RTI average annual growth rate are presented in Table 3-69 for each projection year.

Table 3-69 Regional Emission Inventory Growth Factors for 2020 and 2030 Multiplicative Growth Factor Relative to 2002

Emission Inventory Region

2002-2020 Average

Annualized Growth Rate

(%) 2020 2030 Alaska East (AE) 3.3 1.79 2.48 Alaska West (AW) 3.3 1.79 2.48 East Coast (EC) 4.5 2.21 3.43 Gulf Coast (GC) 2.9 1.67 2.23 Hawaii East (HE) 5.0 2.41 3.92 Hawaii West (HW) 5.0 2.41 3.92 North Pacific (NP) 3.3 1.79 2.48 South Pacific (SP) 5.0 2.41 3.92 Great Lakes (GL) 1.7 1.35 1.60


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3.4.3 Emission Controls in Baseline and Control Scenarios

This section describes the control programs present in the baseline and control scenarios, as well as the resulting emission factors.

The baseline scenario includes the International Marine Organization’s (IMO) Tier 1

NOX standard for marine diesel engines that became effective in 2000. The control scenario applies global controls as well as additional ECA controls within the ECA boundaries

The global NOX controls include a retrofit program for Tier 0 (pre-control) engines, which was modeled as 11 percent control from Tier 0 for 80 percent of 1990 thru 1999 model year (MY) engines greater than 90 liters per cylinder (L/cyl) starting in 2011. The retrofit program was also modeled with a five year phase-in. The current Tier 1 controls, which also are modeled as achieving an 11 percent reduction from Tier 0, apply to the 2000 thru 2010 MY engines. In 2011 thru 2015, Tier 2 controls are applied. Tier 2 controls are modeled as a 2.5 g/kW-hr reduction from Tier 1. Fuel sulfur content for the global control area is assumed to be controlled to 5,000 ppm. No controls are assumed for HC or CO.

Within the ECA areas, additional Tier 3 NOX controls are applied for 2016 MY engines

and beyond. Tier 3 controls are modeled as achieving an 80 percent reduction from Tier 1 levels. In addition to the NOX control program, fuel sulfur content is also assumed to be controlled to 1,000 ppm within the ECA in 2020 and 2030. Fuel sulfur content affects SO2 and PM emissions. Note that gas and steam turbine engines are not subject to any of the NOX standards; however, these engines are not a large part of the inventory.

Within the control scenario, global controls are applied for the Alaska West and Hawaii West regions. Global controls are also applied beyond 200 nm from shore for the 48 contiguous states, Alaska East, and Hawaii East. The ECA controls are applied within 200 nm from shore for the 48 contiguous states as well as the Alaska East and Hawaii East regions.

3.4.3.1 2020 and 2030 Emission Factors

The baseline scenario described in the previous section includes Tier 1 NOX control. The control scenario includes additional NOX controls and fuel sulfur controls, the latter affecting PM and SO2 emissions. The switch to lower sulfur distillate fuel use is also assumed to lower CO2 emissions slightly. HC and CO are assumed to remain unchanged.

The NOX emission factors (EFs) by engine/ship type and tier are provided in Table 3-70. Tier 0 refers to pre-control. There are separate entries for Tier 0/1 base and Tier 0/1 control, since the Tier 0/1 control engines would be using distillate fuel, and there are small NOX emission reductions assumed when switching from residual to distillate fuel.21 The NOX control EFs by tier were derived using the assumptions described in Section 3.4.3.


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Table 3-70 Modeled NOX Emission Factors by Tier

NOX EF (g/kW-hr) Baseline Control Areas

Engine/ Ship Type Tier 0 Tier 1 Tier 0

T0 retrofit a Tier 1 Tier 2 Tier 3

Main SSD 18.1 16.1 17 15.1 15.1 12.6 3

MSD 14 12.5 13.2 11.7 11.7 9.2 2.3 ST 2.1 n/a 2 n/a n/a n/a n/a GT 6.1 n/a 5.7 n/a n/a n/a n/a

Aux Pass 14.6 13.0 14.6 n/a* 13 10.5 2.6

Other 14.5 12.9 14.5 n/a* 12.9 10.4 2.6 Note: a The retrofit program applies to engines over 90 L/cyl; auxiliary engines are smaller than this cutpoint and would therefore not be subject to the program.

The NOX EFs by tier were then used with the age distributions in Table 3-71 and Table

3-72 below to generate calendar year NOX EFs by engine/ship type for the base and control areas included in the scenarios. These calendar year NOX EFs are provided in Table 3-73. Since the age distributions are different for vessels in the Great Lakes, NOX EFs were determined separately for the Great Lakes.


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Table 3-71 Vessel Age Distribution for Deep Sea Ports by Engine Type

Propulsion Engine Type a (Fraction of Total) Age Group (years old) MSD SSD GT ST

All Auxiliary Engines

0 0.00570 0.02667 0.00000 0.00447 0.01958 1 0.07693 0.07741 0.07189 0.12194 0.07670 2 0.10202 0.07512 0.14045 0.16464 0.08426 3 0.08456 0.07195 0.05608 0.05321 0.07489 4 0.08590 0.05504 0.67963 0.00000 0.07831 5 0.06427 0.05563 0.04165 0.00000 0.05685 6 0.06024 0.04042 0.00000 0.00000 0.04455 7 0.07867 0.07266 0.00626 0.00000 0.07150 8 0.06730 0.05763 0.00000 0.00000 0.05764 9 0.04181 0.04871 0.00000 0.00000 0.04475

10 0.04106 0.04777 0.00000 0.00000 0.04364 11 0.03100 0.03828 0.00000 0.00000 0.03538 12 0.04527 0.03888 0.00000 0.04873 0.04160 13 0.03583 0.02787 0.00000 0.00000 0.02909 14 0.03519 0.02824 0.00000 0.00000 0.02935 15 0.02921 0.01466 0.00000 0.00000 0.01869 16 0.00089 0.01660 0.00000 0.00000 0.01189 17 0.01326 0.01582 0.00000 0.00000 0.01462 18 0.00847 0.02414 0.00000 0.00000 0.01966 19 0.00805 0.01982 0.00000 0.00000 0.01550 20 0.00566 0.02258 0.00000 0.00000 0.01756 21 0.00495 0.02945 0.00000 0.00000 0.02260 22 0.00503 0.01883 0.00000 0.00875 0.01467 23 0.00676 0.01080 0.00000 0.00883 0.00943 24 0.00539 0.01091 0.00000 0.00883 0.00900 25 0.01175 0.01099 0.00000 0.18029 0.01224 26 0.00803 0.01045 0.00000 0.11065 0.01130 27 0.00522 0.00835 0.00000 0.01395 0.00738 28 0.00294 0.00788 0.00000 0.08657 0.00659 29 0.00285 0.00370 0.00034 0.02907 0.00349 30 0.00254 0.00106 0.00370 0.05126 0.00193 31 0.00084 0.00113 0.00000 0.00605 0.00096 32 0.00023 0.00367 0.00000 0.07105 0.00322 33 0.00117 0.00582 0.00000 0.00000 0.00419 34 0.00132 0.00092 0.00000 0.00000 0.00098

35+ 0.01967 0.00013 0.00000 0.03172 0.00598 Note:

a MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.


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Table 3-72 Vessel Age Distribution for Great Lake Ports by Engine Type

Propulsion Engine Typea (Fraction of Total) Age Group (years old)

MSD SSD ST All Auxiliary Engines

0 0.01610 0.03913 0.00000 0.02399 1 0.02097 0.03489 0.00000 0.02243 2 0.01370 0.04644 0.00000 0.02544 3 0.02695 0.03040 0.00000 0.02511 4 0.01571 0.04547 0.00000 0.02497 5 0.04584 0.01498 0.00000 0.02442 6 0.01494 0.02180 0.00000 0.01528 7 0.01327 0.01857 0.00000 0.01391 8 0.00099 0.04842 0.00000 0.02107 9 0.00027 0.03376 0.00000 0.01454

10 0.01085 0.01177 0.00000 0.01076 11 0.00553 0.01183 0.00000 0.00782 12 0.00739 0.00546 0.00000 0.00626 13 0.02289 0.02557 0.00000 0.02242 14 0.00000 0.00286 0.00000 0.00121 15 0.00275 0.00510 0.00000 0.00361 16 0.00069 0.00073 0.00000 0.00078 17 0.00000 0.00104 0.00000 0.00041 18 0.00342 0.01967 0.00000 0.01059 19 0.00219 0.01220 0.00000 0.00645 20 0.00867 0.06140 0.00000 0.03034 21 0.00000 0.05638 0.00000 0.02503 22 0.03375 0.02108 0.00000 0.02279 23 0.04270 0.02051 0.00000 0.02606 24 0.08161 0.01010 0.00000 0.03744 25 0.02935 0.05217 0.00000 0.03480 26 0.18511 0.00522 0.00000 0.07701 27 0.01870 0.00389 0.00000 0.01083 28 0.13815 0.01438 0.00000 0.06181 29 0.05487 0.01160 0.00000 0.02697 30 0.00000 0.00114 0.00000 0.00047 31 0.03986 0.00000 0.00000 0.01611 32 0.03654 0.00282 0.00000 0.01631 33 0.03358 0.00000 0.00000 0.01358 34 0.00295 0.00123 0.00000 0.00165

35+ 0.06974 0.30796 1.00000 0.31734 Notes: a MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine. b Fleet average weighted by installed power (ship port calls x main propulsion engine power).


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Table 3-73 Modeled NOX Emission Factors by Calendar Year and Control Type

CY NOX EF (g/kW-hr)

2020 Base 2020 ECA

Control 2020 Global

Control 2030 Base 2030 ECA

Control 2030 Global

Control Engine/ Ship Type 2002 DSPa GLb DSP GL DSP GL DSP GL DSP GL DSP GL Main

SSD 18.1 16.36 17.12 10.80 13.07 13.74 14.95 16.13 16.73 5.68 10.44 13.00 14.20 MSD 14 12.58 13.64 7.72 11.79 10.17 12.44 12.50 12.74 3.58 9.95 9.49 11.44

ST 2.1 2.1 2.1 2.0 2.0 2.0 2.0 2.1 2.1 2.0 2.0 2.0 2.0 GT 6.1 6.1 n/ac 5.7 n/a 5.7 n/a 6.1 n/a 5.7 n/a 5.7 n/a

Aux Pass 14.6 13.21 14.13 8.59 11.99 n/a n/a 13.05 13.61 4.39 10.30 n/a n/a

Other 14.5 13.06 13.97 8.59 11.99 n/a n/a 12.90 13.46 4.39 10.30 n/a n/a Notes: a DSP = Deep sea ports and areas other than the Great Lakes b GL = Great Lakes c n/a = not applicable. There are no GT engines assumed to be operating in the Great Lakes. Auxiliary engines are assumed to be operating in ports and therefore not subject to global controls.

For PM and SO2, there are no proposed standards; however, the control of fuel sulfur affects these pollutants. Therefore, the PM and SO2 EFs are strictly a function of fuel sulfur level. For the baseline portions of the inventory, there are two residual fuel sulfur levels modeled: 25,000 ppm for the West Coast and 27,000 ppm for the rest of the U.S. The baseline distillate fuel sulfur level assumed for all areas is 15,000 ppm. As discussed in Section 3.3.2.3.5, for the baseline, main engines use residual fuel and auxiliary engines use a mix of residual and distillate fuel. For the control areas, there are two levels of distillate fuel sulfur assumed to be used by all engines: 5,000 ppm for the global control areas and 1,000 ppm for the ECA control areas.

Table 3-74 provides the PM10 EFs by engine/ship type and fuel sulfur level. For modeling purposes, PM2.5 is assumed to be 92 percent of PM10. The PM EFs are adjusted to reflect the appropriate fuel sulfur levels using the equation described in Section 3.3.2.3.6.

Table 3-75 provides the modeled SO2 EFs. SO2 emission reductions are directly proportional to reductions in fuel sulfur content.

CO2 is directly proportional to fuel consumed. Table 3-76 provides the modeled CO2 and brake specific fuel consumption (BSFC) EFs. Due to the higher energy content of distillate fuel on a mass basis, the switch to distillate fuel for the control areas results in a small reduction to BSFC and, correspondingly, CO2 emissions.21


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Table 3-74 Modeled PM10 Emission Factors* PM10 EF (g/kW-hr)

Baseline Control Areas

Engine/ Ship Type

Other than West Coast

27,000 ppm S West Coasta

25,000 ppm S ECA

5,000 ppm S Global Control

1,000 ppm S Main

SSD 1.40 1.40 0.31 0.19 MSD 1.40 1.40 0.31 0.19

ST 1.50 1.40 0.35 0.17 GT 1.50 1.40 0.35 0.17

Aux Pass 1.40 1.30 0.31 0.19

Other 1.20 1.10 0.31 0.19 Note: a For the base cases, the West Coast fuel is assumed to be used in the following regions: Alaska East (AE), Alaska West (AW), Hawaii East (HE), Hawaii West (HW), North Pacific (NP), and South Pacific (SP).

Table 3-75 Modeled SO2 Emission Factors* SO2 EF (g/kW-hr)

Baseline Control Areas

Engine/ Ship Type

Other than West Coast

27,000 ppm S West Coasta

25,000 ppm S ECA

5,000 ppm S

Global Control

1,000 ppm S Main

SSD 10.29 9.53 1.81 0.36 MSD 11.09 10.26 1.96 0.39

ST 16.10 14.91 2.83 0.57 GT 16.10 14.91 2.83 0.57

Aux Pass 10.70 9.93 1.96 0.39

Other 9.66 9.07 1.96 0.39 Note: a For the base cases, the West Coast fuel is assumed to be used in the following regions: Alaska East (AE), Alaska West (AW), Hawaii East (HE), Hawaii West (HW), North Pacific (NP), and South Pacific (SP).


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Table 3-76 Modeled Fuel Consumption and CO2 Emission Factors

EF (g/kW-hr) Baseline Control Areas Engine/

Ship Type BSFC CO2 BSFC CO2 Main

SSD 195 620.62 185 588.86 MSD 210 668.36 200 637.05

ST 305 970.71 290 923.07 GT 305 970.71 290 923.07

Aux Pass 210 668.36 200 636.60

Other 210 668.36 200 636.60

3.4.4 Calculation of Near Port and Interport Inventories

Based on the emission factors described in Section 3.4.3.1, appropriate growth factors and emission adjustment factors were applied to the 2002 baseline inventory to obtain the NOX, PM (PM10 and PM2.5), SO2, and CO2 inventory of each 2020 and 2030 scenario. Adjustment factors are ratios of the 2020 or 2030 calendar year EFs to the 2002 calendar year EFs. Adjustment factors are derived separately by engine type for propulsion and auxiliary engines. The adjustment factors for propulsion engines are applied to the propulsion portion of the port inventory and the interport portion of the inventory. The adjustment factors for auxiliary engines are applied to the auxiliary portion of the port inventory. This section describes the development and application of the adjustment factors to the port and interport inventories, and the methodology for combining the port and interport portions.

3.4.4.1 Port Methodology

3.4.4.1.1 Non-California Ports

For the non-California ports, 2002 emissions for each port are summed by engine/ship type. Propulsion and auxiliary emissions are summed separately, since the EF adjustment factors differ. The appropriate regional growth factor, as provided in Table 3-69, is then applied, along with EF adjustment factors by engine/ship type. The EF adjustment factors are a ratio of the control EF to the 2002 EF. Table 3-77 thru Table 3-81 provide the EF adjustment factors for each pollutant and control area. The ports will be subject to ECA controls in the control scenarios. These tables are also used as input for the California ports and interport control inventory development, discussed in subsequent sections. Since the control scenario assumes a portion of the inventory is subject to global controls, the adjustment factors for the 2020 and 2030 global controls are also provided. The baseline adjustment factors are also provided.


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Table 3-77 NOX EF Adjustment Factors by Engine/Ship Type and Control Typea

2020 Base 2020 ECA

Control 2020 Global

Control 2030 Base 2030 ECA

Control 2030 Global

Control Engine/ Ship Type DSPb GLc DSP GL DSP GL DSP GL DSP GL DSP GL Main

SSD 0.9037 0.9459 0.5967 0.7219 0.7592 0.8261 0.8913 0.9243 0.3138 0.5771 0.7183 0.7847 MSD 0.8987 0.9744 0.5515 0.8423 0.7265 0.8883 0.8926 0.9101 0.2559 0.7109 0.6776 0.8170

ST 1.0000 1.0000 0.9524 0.9524 0.9524 0.9524 1.0000 1.0000 0.9524 0.9524 0.9524 0.9524 GT 1.0000 n/a 0.9344 n/a 0.9344 n/a 1.0000 n/a 0.9344 n/a 0.9344 n/a

Aux Pass 0.9025 0.9657 0.5869 0.8196 n/a n/a 0.8917 0.9301 0.3003 0.7042 n/a n/a

Other 0.9025 0.9657 0.5940 0.8295 n/a n/a 0.8917 0.9301 0.3039 0.7127 n/a n/a Notes: a NOX adjustment factors are a ratio of future base or control EFs to 2002 EFs b DSP = deep sea ports and areas other than the Great Lakes c GL = Great Lakes

Table 3-78 PM10 EF Adjustment Factors by Engine/Ship Type and Control Typea

Base ECA Control Global Control Engine/ Ship Type Otherb WCc Other WC Other WC Main

SSD 1.0000 1.0000 0.1352 0.1352 0.2183 0.2183 MSD 1.0000 1.0000 0.1328 0.1328 0.2227 0.2227

ST 1.0000 1.0000 0.1108 0.1187 0.2324 0.2490 GT 1.0000 1.0000 0.1108 0.1187 0.2324 0.2490

Aux Pass 1.0000 1.0000 0.1328 0.1430 0.2227 0.2398

Other 1.0000 1.0000 0.1550 0.1691 0.2598 0.2834 Notes: a PM10 adjustment factors are a ratio of the control EFs to the baseline EFs. PM is not adjusted for the future baselines because fuel sulfur levels are only assumed to change within the ECA and global control areas. b Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, Hawaii, North Pacific, and South Pacific.


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Table 3-79 PM2.5 EF Adjustment Factors by Engine/Ship Type and Control Typea


SSD 1.0000 1.0000 0.1339 0.1339 0.2163 0.2163 MSD 1.0000 1.0000 0.1316 0.1316 0.2207 0.2207

ST 1.0000 1.0000 0.1092 0.1176 0.2291 0.2467 GT 1.0000 1.0000 0.1092 0.1176 0.2291 0.2467

Aux Pass 1.0000 1.0000 0.1316 0.1426 0.2207 0.2390

Other 1.0000 1.0000 0.1555 0.1711 0.2608 0.2868 Notes: a PM2.5 adjustment factors are a ratio of the control EFs to the baseline EFs. PM is not adjusted for the future baselines because fuel sulfur levels are only assumed to change within the ECA and global control areas. The PM2.5 adjustment factors are slightly different from those for PM10 due to rounding. b Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, Hawaii, North Pacific, and South Pacific.

Table 3-80 SO2 EF Adjustment Factors by Engine/Ship Type and Control Typea Base ECA Control Global Control Engine/

Ship Type Otherb WCc Other WC Other WC Main

SSD 1.0000 1.0000 0.0351 0.0380 0.1757 0.1898 MSD 1.0000 1.0000 0.0353 0.0381 0.1764 0.1907

ST 1.0000 1.0000 0.0352 0.0380 0.1761 0.1901 GT 1.0000 1.0000 0.0352 0.0380 0.1761 0.1901

Aux Pass 1.0000 1.0000 0.0365 0.0394 0.1827 0.1969

Other 1.0000 1.0000 0.0405 0.0431 0.2024 0.2156 Notes: a SO2 adjustment factors are a ratio of the control EFs to the baseline EFs. SO2 is not adjusted for the future baselines because fuel sulfur levels are only assumed to change within the ECA and global control areas. b Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, Hawaii, North Pacific, and South Pacific.


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Table 3-81 CO2 EF Adjustment Factors by Engine/Ship Type and Control Typea


SSD 1.0000 1.0000 0.9488 0.9488 0.9488 0.9488 MSD 1.0000 1.0000 0.9531 0.9531 0.9531 0.9531

ST 1.0000 1.0000 0.9509 0.9509 0.9509 0.9509 GT 1.0000 1.0000 0.9509 0.9509 0.9509 0.9509

Aux Pass 1.0000 1.0000 0.9525 0.9593 0.9525 0.9593

Other 1.0000 1.0000 0.9525 0.9683 0.9525 0.9683 Notes: a CO2 adjustment factors are a ratio of the control EFs to the baseline EFs. CO2 is not adjusted for the future baselines because fuel consumption (BSFC) is only assumed to change within the ECA and global control areas. b Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, Hawaii, North Pacific, and South Pacific.

3.4.4.1.2 California Ports

For the California ports, 2002 emissions for each port are summed by ship type. Propulsion and auxiliary emissions are summed separately, since the EF adjustment factors differ. The EF adjustment factors by engine/ship type, provided in the previous section, are consolidated by ship type, using the CARB assumption that engines on all ships except passenger ships are 95 percent slow speed diesel (SSD) engines and 5 percent medium speed diesel engines (MSD) based upon a 2005 ARB survey.53 All passenger ships were assumed to be medium speed diesel engines with electric drive propulsion (MSD-ED). Steam turbines (ST) and gas-turbines (GT) are not included in the CARB inventory. The EF adjustment factors by ship type are then applied, along with ship-specific growth factors supplied by CARB. The ship-specific growth factors relative to 2002 are provided in Table 3-82 below.

Table 3-82 Growth Factors by Ship Type for California Ports Relative to 2002 Calendar Year

Ship Type 2002 2020 2030Auto 1.0000 1.5010 1.8478Bulk 1.0000 0.2918 0.1428Container 1.0000 2.5861 4.2828General 1.0000 0.7331 0.5985Passenger 1.0000 7.5764 26.4448Reefer 1.0000 1.0339 1.0532RoRo 1.0000 1.5010 1.8478Tanker 1.0000 2.0979 3.0806

Deleted: 52.


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3.4.4.2 Interport Methodology

The interport portion of the inventory is not segregated by engine or ship type. As a result, regional EF adjustment factors were developed based on the assumed mix of main (propulsion) engine types in each region. The mix of main engine types by region was developed using the ship call and power data and is presented in Table 3-83 and Figure 3-16. Main engines are considered a good surrogate for interport emissions, since the majority of emissions while underway are due to the main engines. The EF adjustment factors by main engine type in Section 3.4.4.1.1 were used together with the mix of main engine types by region to develop the EF regional adjustment factors for each control area. The resulting EF regional adjustment factors for each pollutant and control area are provided in Table 3-84 thru Table 3-88 below. These EF regional adjustment factors, together with the regional growth factors in Table 3-69, were applied to calculate the future inventories for each control area.

Table 3-83 Installed Power by Main Engine Type for Deep Sea Ports a 2020 Installed Power (%) 2030 Installed Power (%)

Region MSD SSD GT ST Total MSD SSD GT ST Total Alaska East (AE) 19.1% 18.4% 0.3% 62.2% 0.8% 19.1% 18.4% 0.3% 62.2% 0.6% Alaska West (AW) 19.1% 18.4% 0.3% 62.2% 0.8% 19.1% 18.4% 0.3% 62.2% 0.6% East Coast (EC) 25.6% 72.5% 0.9% 1.0% 45.4% 25.6% 72.5% 0.9% 1.0% 42.3% Gulf Coast (GC) 13.7% 85.5% 0.0% 0.8% 16.8% 13.7% 85.5% 0.0% 0.8% 13.4% Hawaii East(HE) 66.2% 18.5% 7.4% 8.0% 2.0% 66.2% 18.5% 7.4% 8.0% 2.0% Hawaii West (HW) 66.2% 18.5% 7.4% 8.0% 2.0% 66.2% 18.5% 7.4% 8.0% 2.0% North Pacific (NP) 5.1% 83.5% 1.6% 9.7% 5.0% 5.1% 83.5% 1.6% 9.7% 4.1% South Pacific (SP) 29.2% 70.8% 0.0% 0.0% 30.0% 45.5% 54.5% 0.0% 0.0% 37.6%

Note: a Installed power is main propulsion engine power (kW) multiplied by ship port calls by engine type. MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.

MSD48%

SSD44%

ST8%

Figure 3-16 Installed Power by Main Engine Type for Great Lake Ports D

D Installed power is main propulsion engine power (kW) multiplied by ship port calls by engine type. MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.


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Table 3-84 NOX EF Adjustment Factors by Region and Control Typea

2020 2030

U.S. Region 2002 Base ECA

Control Global Control Base

ECA Control

Global Control

Alaska East (AE) 1.0000 0.9629 0.8104 n/a 0.9595 0.7019 n/a Alaska West (AW) 1.0000 0.9629 n/a 0.8737 0.9595 n/a 0.8568 East Coast (EC) 1.0000 0.9042 0.5917 n/a 0.8937 0.3110 n/a Gulf Coast (GC) 1.0000 0.9038 0.5935 n/a 0.8924 0.3113 n/a Hawaii East (HE) 1.0000 0.9152 0.6201 n/a 0.9088 0.3723 n/a Hawaii West (HW) 1.0000 0.9152 n/a 0.7659 0.9088 n/a 0.7260 North Pacific (NP) 1.0000 0.9143 0.6343 n/a 0.9036 0.3828 n/a South Pacific (SP) 1.0000 0.9022 0.5837 n/a 0.8919 0.2877 n/a Great Lakes (GL) 1.0000 0.9641 0.7989 n/a 0.9238 0.6726 n/a Out of Regionb 1.0000 0.8942 n/a 0.7557 0.8940 n/a 0.7103

Notes: a NOX adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment factors are used to adjust the interport portion of the 2002 inventory. b Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion power in each region. ECA control is only assumed within 200nm.

Table 3-85 PM10 EF Adjustment Factors by Region and Control Typea 2020 2030



ECA Control

Global Control


Notes: a PM10 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment factors are used to adjust the interport portion of the 2002 inventory. b Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion power in each region. ECA control is only assumed within 200nm.


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Table 3-86 PM2.5 EF Adjustment Factors by Region and Control Typea

2020 2030



ECA Control

Global Control

Alaska East (AE) 1.0000 1.0000 0.1233 n/a 1.0000 0.1233 n/a Alaska West (AW) 1.0000 1.0000 n/a 0.2252 1.0000 n/a 0.2252 East Coast (EC) 1.0000 1.0000 0.1329 n/a 1.0000 0.1329 n/a Gulf Coast (GC) 1.0000 1.0000 0.1334 n/a 1.0000 0.1334 n/a Hawaii East (HE) 1.0000 1.0000 0.1299 n/a 1.0000 0.1299 n/a Hawaii West (HW) 1.0000 1.0000 n/a 0.2225 1.0000 n/a 0.2225 North Pacific (NP) 1.0000 1.0000 0.1320 n/a 1.0000 0.1320 n/a South Pacific (SP) 1.0000 1.0000 0.1332 n/a 1.0000 0.1329 n/a Great Lakes (GL) 1.0000 1.0000 0.1307 n/a 1.0000 0.1307 n/a Out of Regionb 1.0000 1.0000 n/a 0.2177 1.0000 n/a 0.2180 Notes: a PM2.5 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment factors are used to adjust the interport portion of the 2002 inventory. b Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion power in each region. ECA control is only assumed within 200nm.

Table 3-87 SO2 EF Adjustment Factors by Region and Control Typea 2020 2030



ECA Control

Global Control

Alaska East (AE) 1.0000 1.0000 0.0380 n/a 1.0000 0.0380 n/a Alaska West (AW) 1.0000 1.0000 n/a 0.1814 1.0000 n/a 0.1814 East Coast (EC) 1.0000 1.0000 0.0352 n/a 1.0000 0.0352 n/a Gulf Coast (GC) 1.0000 1.0000 0.0352 n/a 1.0000 0.0352 n/a Hawaii East (HE) 1.0000 1.0000 0.0381 n/a 1.0000 0.0381 n/a Hawaii West (HW) 1.0000 1.0000 n/a 0.1893 1.0000 n/a 0.1893 North Pacific (NP) 1.0000 1.0000 0.0380 n/a 1.0000 0.0380 n/a South Pacific (SP) 1.0000 1.0000 0.0380 n/a 1.0000 0.0380 n/a Great Lakes (GL) 1.0000 1.0000 0.0352 n/a 1.0000 0.0352 n/a Out of Regionb 1.0000 1.0000 n/a 0.1811 1.0000 n/a 0.1821 Notes: a SO2 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment factors are used to adjust the interport portion of the 2002 inventory. b Out of Region refers to areas outside the 200nm, but within the air quality modeling domain. The out of region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion power in each region. ECA control is only assumed within 200nm.


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Table 3-88 CO2 EF Adjustment Factors by Region and Control Typea

2020 2030



ECA Control

Global Control


Notes: a CO2 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment factors are used to adjust the interport portion of the 2002 inventory. b Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion power in each region. ECA control is only assumed within 200nm.

3.4.4.3 Estimating and Combining the Near Port and Interport Inventories

To produce future year control scenarios, the interport inventories were scaled by a growth factor to 2020 and 2030, as previously described. An ECA boundary line was drawn so that each point on it was at a 200 nm distance from the nearest point on land. Adjustment factors, as described in Section 3.4.3.1, were then applied to interport emissions within the ECA boundary.

To create control scenarios in the near port inventories, growth and control factors were applied to the 2002 near port inventories (described in Sections 3.4.2 and 3.4.3.1). The near port inventories were then converted into a gridded format (Section 3.3.3.3). Using this grid, STEEM values were removed from near port cells and near port emissions were used as replacement values. In cases where the emissions near ports were only partially attributable to port traffic, the STEEM inventory was reduced rather than removed.

Interport and near port emissions were then aggregated to form regional totals.

3.4.5 2020 and 2030 Baseline Inventories

The resulting 2020 and 2030 estimated emission inventories by region and the nation are shown in Table 3-89 and Table 3-90. These baseline inventories account for growth as well as


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implementation of the Tier 1 NOX standard. Estimated fuel consumption for the baseline inventories by region and fuel type is given in Table 3-91.

Table 3-89 2020 Baseline Emissions Inventory

Metric Tonnes per Year

U.S. Region NOX PM10 PM2.5a HC CO SO2 CO2

Alaska East (AE) 29,242 2,561 2,356 1,073 2,534 19,084 1,182,047 Alaska West (AW) 93,685 8,118 7,469 3,444 8,112 60,227 3,711,596 East Coast (EC) 439,604 39,003 35,882 16,216 38,382 323,038 18,121,202 Gulf Coast (GC) 259,295 23,403 21,531 9,590 23,628 174,751 10,567,512 Hawaii East (HE) 48,026 4,185 3,850 1,765 4,161 31,075 1,930,172 Hawaii West (HW) 67,573 5,888 5,417 2,483 5,855 43,722 2,715,741 North Pacific (NP) 42,644 3,916 3,603 1,706 3,799 27,807 1,800,743 South Pacific (SP) 234,968 20,148 18,536 8,585 20,686 149,751 9,490,502 Great Lakes (GL) 19,842 1,613 1,484 681.914 1,607 11,993 740,624 Total U.S. Metric Tonnes 1,234,879 108,835 100,128 45,544 108,762 841,447 50,260,140

Total U.S. Short Tons b 1,361,221 119,970 110,372 50,204 119,890 927,537 55,402,321 Notes: a Estimated from PM10 using a multiplicative conversion factor of 0.92. b Converted from metric tonnes using a multiplicative conversion factor of 1.102 short tons per metric tonne.

Table 3-90 2030 Baseline Emissions Inventory



Alaska East (AE) 42,930 3,544 3,260 1,485 3,505 26,404 1,635,479 Alaska West (AW) 137,951 11,232 10,333 4,765 11,223 83,329 5,135,278 East Coast (EC) 679,271 60,615 55,766 25,207 59,678 502,305 28,163,780 Gulf Coast (GC) 341,903 31,142 28,651 12,761 31,427 232,547 14,062,207 Hawaii East (HE) 78,806 6,818 6,273 2,875 6,780 50,630 3,144,932 Hawaii West (HW) 110,880 9,593 8,825 4,045 9,539 71,237 4,424,900 North Pacific (NP) 58,937 5,433 4,999 2,372 5,278 38,556 2,497,078 South Pacific (SP) 394,335 34,948 32,152 14,635 35,208 259,982 16,470,350 Great Lakes (GL) 22,471 1,910 1,757 807 1,902 14,196 876,636 Total U.S. Metric Tonnes 1,867,484 165,235 152,016 68,951 164,539 1,279,185 76,410,639

Total U.S. Short Tons b 2,058,549 182,140 167,569 76,006 181,373 1,410,061 84,228,311 Notes: a Estimated from PM10 using a multiplicative conversion factor of 0.92. b Converted from metric tonnes using a multiplicative conversion factor of 1.102 short tons per metric tonne.


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Table 3-91 Fuel Consumption by Category 3 Vessels in Baseline Scenarios

Metric Tonnes Fuel

2020 Baseline 2030 Baseline U.S. Region Distillate Residual Total Distillate Residual Total

Alaska East (AE) 3,386 367,977 371,363 4,685 509,132 513,817 Alaska West (AW) 0 1,166,068 1,166,068 0 1,613,345 1,613,345 East Coast (EC) 202,139 5,490,981 5,693,120 313,916 8,534,271 8,848,187 Gulf Coast (GC) 96,428 3,223,557 3,319,985 128,338 4,289,571 4,417,910 Hawaii East (HE) 10,529 595,871 606,400 17,151 970,889 988,040 Hawaii West (HW) 0 853,202 853,202 0 1,390,166 1,390,166 North Pacific (NP) 28,532 537,206 565,738 39,476 745,028 784,505 South Pacific (SP) 83,576 2,898,045 2,981,622 157,878 5,016,595 5,174,474 Great Lakes (GL) 1,269 231,412 232,681 2,037 273,375 275,412 Total U.S. Metric Tonnes 425,860 15,364,319 15,790,179 663,482 23,342,374 24,005,856

Total U.S. Short Tons 469,431 16,936,262 17,405,693 731,364 25,730,563 26,461,926

3.4.6 2020 and 2030 Control Inventories

For the control scenario, the inventories for each of the nine geographic regions, the U.S. total, and the 48-state total are presented in Table 3-92 and Table 3-93. The regional and total inventories include all emissions within 200nm of shore. For the purposes of this analysis, ECA controls are assumed to apply to all regions, except Alaska West and Hawaii West. For the Alaska West and Hawaii West regions, global controls apply. Estimated fuel consumption for the control inventories by region and fuel type is given in Table 3-94.


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Table 3-92 Category 3 Vessel Inventories for 2020 Control Casea



Alaska East (AE) 25,978 322 296 1,072 2,534 728 1,124,652 Alaska West (AW) 90,787 1,851 1,703 3,444 8,112 10,927 3,529,505 East Coast (EC) 289,671 5,286 4,863 16,231 38,421 11,514 17,233,800 Gulf Coast (GC) 170,861 3,201 2,945 9,581 23,615 6,255 10,034,946 Hawaii East (HE) 32,952 551 507 1,764 4,162 1,187 1,838,832 Hawaii West (HW) 57,406 1,323 1,217 2,483 5,855 8,277 2,585,222 North Pacific (NP) 29,105 539 496 1,709 3,803 1,076 1,715,210 South Pacific (SP) 150,461 2,753 2,533 8,546 20,585 5,786 9,009,986 Great Lakes (GL) 16,420 207 190 676 1,602 420 704,390 Total U.S. Metric Tonnes 863,642 16,032 14,750 45,507 108,688 46,168 47,776,542

Total U.S. Short Tons b 952,002 17,673 16,259 50,163 119,808 50,892 52,664,623 Note: a This scenario assumes ECA controls apply within 200 nautical miles of all U.S. regions except Alaska West and Hawaii West, with global controls applied in all other areas. Corrected boundaries are used.

Table 3-93 Category 3 Vessel Inventories for 2030 Control Casea Metric Tonnes per Year


Alaska East (AE) 30,722 445 410 1,485 3,505 1,008 1,556,045 Alaska West (AW) 123,187 2,677 2,463 4,765 11,223 15,847 4,883,341 East Coast (EC) 235,378 8,221 7,563 25,207 59,678 17,896 26,763,558 Gulf Coast (GC) 118,930 4,261 3,920 12,761 31,426 8,325 13,355,741 Hawaii East (HE) 31,992 899 827 2,875 6,780 1,933 2,995,263 Hawaii West (HW) 88,502 2,175 2,001 4,045 9,539 13,596 4,214,197 North Pacific (NP) 22,758 751 691 2,372 5,278 1,494 2,378,683 South Pacific (SP) 128,302 4,769 4,388 14,635 35,202 10,030 15,713,679 Great Lakes (GL) 16,369 253 233 807 1,902 501 833,733 Total U.S. Metric Tonnes 796,140 24,451 22,495 68,951 164,539 70,630 72,694,239

Total U.S. Short Tons b 877,594 26,953 24,797 76,006 181,373 77,856 80,131,682 Note: a This scenario assumes ECA controls apply within 200 nautical miles of all U.S. regions, except Alaska West and Hawaii West, with global controls elsewhere. Corrected boundaries are used.


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Table 3-94 Fuel Consumption by Category 3 Vessels in Control Scenarios

Metric Tonnes Fuel

2020 Control 2030 Control U.S. Region Distillate Residual Total Distillate Residual Total

Alaska East (AE) 353,331 0 353,331 488,861 0 488,861 Alaska West (AW) 1,108,861 0 1,108,861 1,534,194 0 1,534,194 East Coast (EC) 5,414,326 0 5,414,326 8,408,281 0 8,408,281 Gulf Coast (GC) 3,152,669 0 3,152,669 4,195,960 0 4,195,960 Hawaii East (HE) 577,704 0 577,704 941,019 0 941,019 Hawaii West (HW) 812,197 0 812,197 1,323,970 0 1,323,970 North Pacific (NP) 538,866 0 538,866 747,309 0 747,309 South Pacific (SP) 2,830,658 0 2,830,658 4,936,751 0 4,936,751 Great Lakes (GL) 221,297 0 221,297 261,933 0 261,933 Total U.S. Metric Tonnes 15,009,910 0 15,009,910 22,838,278 0 22,838,278

Total U.S. Short Tons 16,545,593 0 16,545,593 25,174,892 0 25,174,892

3.5 Estimated Category 3 Inventory Contribution

This section describes the contribution of Category 3 marine engines to national and selected local emission inventories in 2002, 2020, and 2030. The pollutants analyzed are NOX, directly emitted PM2.5, and SO2. All weight units in the following tables are short tons.

3.5.1 Baseline Contribution of C3 Vessels to National Level Inventory

Category 3 marine engines contribute to the formation of ground level ozone and concentrations of fine particles in the ambient atmosphere. Based on our current emission inventory analysis, we estimate that these engines contributed nearly 6 percent of mobile source NOX, over 10 percent of mobile source PM2.5, and about 40 percent of mobile source SO2 in 2002. We estimate that their contribution will increase to about 40 percent of mobile source NOX, 48 percent of mobile source PM2.5, and 95 percent of mobile source SO2 by 2030 without further controls on these engines. Our current estimates for NOX, PM2.5, and SO2 inventories are set out in the following tables. Inventory projections for 2020 and 2030 include the effect of existing emission mobile source and stationary source control programs previously adopted by EPA.


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Table 3-95 50 State Annual NOX Baseline Emission Levels for Mobile and Other Source Categories

2002 2020 2030

Category short tons

% of mobile source

% of total short tons

% of mobile source


% of mobile source

% of total

Commercial Marine (C3) 738,700 5.8 3.5 1,361,221 24.4 12.0 2,058,549 39.8 18.8 Locomotive 1,118,786 8.8 5.2 669,405 12.0 5.9 437,245 8.4 4.0 Recreational Marine Diesel 40,437 0.3 0.2 43,579 0.8 0.4 43,665 0.8 0.4 Commercial Marine (C1 & C2) 834,025 6.6 3.9 499,798 8.9 4.4 308,614 6.0 2.8 Land-Based Nonroad Diesel 1,555,812 12.2 7.3 683,481 12.2 6.0 435,774 8.4 4.0 Small Nonroad SI 119,833 0.9 0.6 80,901 1.4 0.7 91,913 1.8 0.8 Recreational Marine SI 49,902 0.4 0.2 87,709 1.6 0.8 73,961 1.4 0.7 SI Recreational Vehicles 10,614 0.1 0.0 30,108 0.5 0.3 34,318 0.7 0.3 Large Nonroad SI (>25hp) 336,292 2.6 1.6 48,270 0.9 0.4 47,766 0.9 0.4 Aircraft 103,591 0.8 0.5 132,278 2.4 1.2 143,986 2.8 1.3 Total Off Highway 4,907,990 38.6 23.0 3,636,750 65.1 32.0 3,675,790 71.0 33.6 Highway Diesel 3,529,046 27.7 16.5 681,142 12.2 6.0 355,817 6.9 3.2 Highway non-diesel 4,293,733 33.7 20.1 1,270,269 22.7 11.2 1,144,199 22.1 10.4 Total Highway 7,822,779 61.4 36.7 1,951,411 34.9 17.2 1,500,016 29.0 13.7 Total Mobile Sources 12,730,769 100.0 59.6 5,588,160 100.0 49.2 5,175,806 100.0 47.3 Stationary Point & Area Sources 8,613,718 - 40.4 5,773,927 - 50.8 5,773,927 - 52.7 Total Man-Made Sources 21,344,488 - 100 11,362,088 - 100 10,949,734 - 100


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Table 3-96 50 State Annual PM2.5 Baseline Emission Levels for Mobile and Other Source Categories 2002 2020 2030

Category short tons

% of diesel mobile


% of diesel mobile


% of diesel mobile

% of total

Commercial Marine (C3) 54,112 14.7 1.5 110,372 52.9 3.3 167,569 74.8 4.9 Locomotive 29,660 8.1 0.8 15,145 7.3 0.4 8,584 3.8 0.3 Recreational Marine Diesel 1,096 0.3 0.0 973 0.5 0.0 1,053 0.5 0.0 Commercial Marine (C1 & C2) 28,730 7.8 0.8 15,787 7.6 0.5 10,017 4.5 0.3 Land-Based Nonroad Diesel 159,111 43.3 4.5 46,056 22.1 1.4 17,902 8.0 0.5 Small Nonroad SI 25,700 0.7 31,981 0.9 36,795 1.1 Recreational Marine SI 16,262 0.5 2,845 0.1 1,225 0.0 SI Recreational Vehicles 13,710 0.4 11,901 0.4 10,090 0.3 Large Nonroad SI (>25hp) 1,652 0.0 2,421 0.1 2,844 0.1 Aircraft 17,979 0.5 22,176 0.7 24,058 0.7 Total Off Highway 348,013 9.9 259,656 7.7 280,136 8.2 Highway Diesel 94,982 25.8 2.7 20,145 9.7 0.6 18,802 8.4 0.6 Highway non-diesel 51,694 1.5 45,329 1.3 51,621 1.5 Total Highway 146,676 4.2 65,474 1.9 70,423 2.1 Total Mobile Sources 494,690 14.1 325,131 9.6 350,559 10.3 Stationary Point & Area Sources 3,025,244 85.9 3,047,714 90.4 3,047,714 89.7 Total Man-Made Sources 3,519,933 100 3,372,845 100 3,398,274 100


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Table 3-97 50 State Annual SO2 Baseline Emission Levels for Mobile and Other Source Categories 2002 2020 2030

Category short tons

% of mobile source


% of mobile source


% of mobile source

% of total

Commercial Marine (C3) 453,614 43.2 3.0 927,537 93.3 10.5 1,410,061 94.9 15.1 Locomotive 75,385 7.2 0.5 396 0.0 0.0 464 0.0 0.0 Recreational Marine Diesel 5,145 0.5 0.0 162 0.0 0.0 192 0.0 0.0 Commercial Marine (C1 & C2) 80,353 7.6 0.5 2,961 0.3 0.0 3,002 0.2 0.0 Land-Based Nonroad Diesel 172,304 16.4 1.2 999 0.1 0.0 1,079 0.1 0.0 Small Nonroad SI 6,742 0.6 0.0 8,870 0.9 0.1 10,282 0.7 0.1 Recreational Marine SI 2,755 0.3 0.0 2,995 0.3 0.0 3,184 0.2 0.0 SI Recreational Vehicles 1,530 0.1 0.0 2,862 0.3 0.0 3,019 0.2 0.0 Large Nonroad SI (>25hp) 933 0.1 0.0 905 0.1 0.0 1,020 0.1 0.0 Aircraft 8,701 0.8 0.1 11,171 1.1 0.1 12,197 0.8 0.1 Total Off Highway 807,463 76.9 5.4 958,857 96.5 10.8 1,444,498 97.2 15.4 Highway Diesel 71,147 6.8 0.5 4,218 0.4 0.0 5,478 0.4 0.1 Highway non-diesel 171,866 16.4 1.1 30,922 3.1 0.3 36,011 2.4 0.4 Total Highway 243,013 23.1 1.6 35,140 3.5 0.4 41,489 2.8 0.4 Total Mobile Sources 1,050,475 100.0 7.0 993,998 100.0 11.2 1,485,986 100.0 15.9 Stationary Point & Area Sources 13,897,968 - 93.0 7,864,681 - 88.8 7,864,681 - 84.1 Total Man-Made Sources 14,948,443 - 100 8,858,678 - 100 9,350,667 - 100


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3.5.2 Contribution to Mobile Source Inventories for Selected Cities

Commercial marine vessels, powered by Category 3 marine engines, contribute significantly to the emissions inventory for many U.S. ports. This is illustrated in Table 3-98, which presents the mobile source inventory contributions of these vessels for several ports. The ports in this table were selected to present a sampling over a wide geographic area along the U.S. coasts. In 2005, these twenty ports received approximately 60 percent of the vessel calls to the U.S. from ships of 10,000 dead weight tons (DWT) or greater.54

Table 3-98 Contribution of Commercial Marine Vessels to Mobile Source Inventories for Selected Ports in 2002a

Port Area % of total

NOX % of total

PM2.5 % of total

SO2 Valdez, AK 4 10 43Seattle, WA 10 20 56Tacoma, WA 20 38 74San Francisco, CA 1 1 31Oakland, CA 8 14 80LA/Long Beach, CA 5 10 71Beaumont, TX 6 20 55Galveston, TX 5 12 47Houston, TX 3 10 41New Orleans, LA 14 24 59South Louisiana, LA 12 24 58Miami, FL 13 25 66Port Everglades, FL 9 20 56Jacksonville, FL 5 11 52Savannah, GA 24 39 80Charleston, SC 22 33 87Wilmington, NC 7 16 73Baltimore, MD 12 27 69New York/New Jersey 4 9 39Boston, MA 4 5 30Note: a This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used on ocean-going vessels.

Currently, more than 40 major U.S. deep sea ports are located in areas that are designated as being in nonattainment for either or both the 8-hour ozone NAAQS and PM2.5 NAAQS. Many ports are located in areas rated as class I federal areas for visibility impairment and regional haze. It should be noted that emissions from ocean-going vessels are not simply a localized problem related only to cities that have commercial ports. Virtually all U.S. coastal areas are affected by emissions from ships that transit between those ports, using shipping lanes that are close to land. Many of these coastal areas also have high population densities. For example, Santa Barbara, which has no commercial port, estimates that engines on ocean-going marine vessels currently contribute about 37 percent of total NOX in their area.55 These


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emissions are from ships that transit the area, and “are comparable to (even slightly larger than) the amount of NOX produced onshore by cars and truck.” By 2015 these emissions are expected to increase 67 percent, contributing 61 percent of Santa Barbara’s total NOX emissions. This mix of emission sources led Santa Barbara to point out that they will be unable to meet air quality standards for ozone without significant emission reductions from these vessels, even if they completely eliminate all other sources of pollution. Interport emissions from OGV also contribute to other environmental problems, affecting sensitive marine and land ecosystems.

3.6 Projected Emission Reductions

The projected tons reductions for each of the 2020 control cases relative to the 2020 baseline, as well as the tons reductions for the 2030 control case relative to the 2030 baseline, are presented in Table 3-99 thru Table 3-100. Reductions by region, for the total U.S., and for the total 48-states, are provided by pollutant in each table.

Table 3-99 Reductions for 2020 Control Casea Metric Tonnes per Year


Alaska East (AE) 3,264 2,239 2,060 0 0 18,356 57,395 Alaska West (AW) 2,897 6,267 5,766 0 0 49,300 182,091 East Coast (EC) 149,933 33,717 31,020 0 0 311,523 887,402 Gulf Coast (GC) 88,434 20,202 18,586 0 0 168,496 532,567 Hawaii East (HE) 15,074 3,634 3,343 0 0 29,888 91,340 Hawaii West (HW) 10,166 4,565 4,200 0 0 35,445 130,519 North Pacific (NP) 13,539 3,377 3,107 0 0 26,731 85,533 South Pacific (SP) 84,507 17,395 16,003 0 0 143,965 480,516 Great Lakes (GL) 3,422 1,406 1,294 0 0 11,574 36,235 Total U.S. Metric Tonnes 371,237 92,803 85,378 0 0 795,279 2,483,598

Total U.S. Short Tons 409,219 102,297 94,114 0 0 876,645 2,737,698 Note: a The emission reductions are relative to the 2020 baseline.


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Table 3-100 Reductions for 2030 Control Casea

Metric Tonnes per Year U.S. Region NOX PM10 PM2.5

a HC CO SO2 CO2 Alaska East (AE) 12,208 3,099 2,851 0 0 25,397 79,434 Alaska West (AW) 14,764 8,555 7,870 0 0 67,482 251,937 East Coast (EC) 443,893 52,394 48,203 0 0 484,409 1,400,222 Gulf Coast (GC) 222,973 26,881 24,731 0 0 224,221 706,466 Hawaii East (HE) 46,814 5,919 5,446 0 0 48,698 149,669 Hawaii West (HW) 22,377 7,417 6,824 0 0 57,641 210,703 North Pacific (NP) 36,179 4,683 4,308 0 0 37,062 118,395 South Pacific (SP) 266,033 30,179 27,764 0 6 249,952 756,671 Great Lakes (GL) 6,102 1,657 1,524 0 0 13,694 42,904 Total U.S. Metric Tonnes 1,071,344 140,783 129,521 0 0 1,208,555 3,716,400

Total U.S. Short Tons 1,180,955 155,187 142,772 0 0 1,332,204 4,096,630 Note: a The emission reductions are relative to the 2030 baseline.

3.7 Inventories Used for Air Quality Modeling

The emission inventories for 2020 presented in this chapter are slightly different from the emissions inventories used in the air quality modeling presented in Chapter 2. Specifically, the 2020 inventories used in the air quality modeling reflect a slightly different boundary for the proposed ECA that was based on a measurement error. Due to the nature of the measurement error, the corrections to the ECA boundaries are not uniform, but are different by coastal area. The measurement error affects only those portions that are farthest from shore. The 2030 inventories are not affected by this error.

A comparison of the air quality and final inventories by region for the 2020 baseline scenario is provided in Table 3-101. Results are provided only for NOX, PM2.5, and SO2, since the air quality modeling is focused on ozone and PM2.5. In addition, Alaska and Hawaii are not included, since the air quality modeling domain does not include these states. As seen in Table 3-101, the changes due to the boundary error are not expected to have a significant impact on the results of our analysis.


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Table 3-101 Comparison of Air Quality versus Final Inventories for 2020 Baseline Case

Metric Tonnes per Year NOX PM2.5 SO2

U.S. Region AQ Final %

Diff AQ Final %

Diff AQ Final %

Diff East Coast (EC) 439,713 439,604 0% 35,891 35,882 0% 323,108 323,038 0% Gulf Coast (GC) 261,024 259,295 1% 21,669 21,531 1% 175,862 174,751 1% North Pacific (NP) 42,291 42,644 -1% 3,575 3,603 -1% 27,580 27,807 -1% South Pacific (SP) 216,849 234,968 -8% 17,092 18,536 -8% 138,102 149,751 -8% Great Lakes (GL) 19,842 19,842 0% 1,484 1,484 0% 11,993 11,993 0% Total 48-State 979,719 996,353 -2% 79,711 81,036 -2% 676,645 687,340 -2%

The 2020 control inventories are also subject to the boundary error. In addition, the 2020 air quality control case does not include global controls for areas that are beyond 200 nm but within the air quality modeling domain. The impact of this latter difference is expected to be minimal.

The modeling for 2030 was based on inventories that reflected an ECA distance closer to shore than what we are proposing. The air quality modeling, and related estimates of benefits, therefore reflect the impacts associated with approximately 80% of the emission reductions achieved by the proposed coordinated strategy. As a result, the 2030 air quality impacts and health benefits presented in Chapters 2 and 6, respectively, should be considered conservative estimates of the improvements in air quality associated with the proposal. For the final RIA, we plan to model the 2030 coordinated strategy to control ship emissions with a 200 nm boundary and global controls beyond.

Deleted: 200


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APPENDIX 3A

Port Coordinates and Reduced Speed Zone Information


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Table 3-102 Port Coordinates Port Coordinates

Port Name US ACE

Code Longitude Latitude

Albany, NY C0505 -73.7482 42.64271Alpena, MI L3617 -83.4223 45.0556Anacortes, WA C4730 -122.6 48.49617Anchorage, AK C4820 -149.895 61.23778Ashtabula, OH L3219 -80.7917 41.91873Baltimore, MD C0700 -76.5171 39.20899Barbers Point, Oahu, HI C4458 -158.109 21.29723Baton Rouge, LA C2252 -91.1993 30.42292Beaumont, TX C2395 -94.0881 30.08716Boston, MA C0149 -71.0523 42.35094Bridgeport, CT C0311 -73.1789 41.172Brownsville, TX C2420 -97.3981 25.9522Brunswick, GA C0780 -81.4999 31.15856Buffalo, NY L3230 -78.8953 42.8783Burns Waterway Harbor, IN L3739 -87.1552 41.64325Calcite, MI L3620 -83.7756 45.39293Camden-Gloucester, NJ C0551 -75.1043 39.94305Carquinez, CA CCA01 -122.123 38.03556Catalina, CA CCA02 -118.496 33.43943Charleston, SC C0773 -79.9216 32.78878Chester, PA C0297 -75.3222 39.85423Chicago, IL L3749 -87.638 41.88662Cleveland, OH L3217 -81.6719 41.47852Conneaut, OH L3220 -80.5486 41.96671Coos Bay, OR C4660 -124.21 43.36351Corpus Christi, TX C2423 -97.3979 27.81277Detroit, MI L3321 -83.1096 42.26909Duluth-Superior, MN and WI L3924 -92.0964 46.77836El Segundo, CA CCA03 -118.425 33.91354Erie, PA L3221 -80.0679 42.15154Escanaba, MI L3795 -87.025 45.73351Eureka, CA CCA04 -124.186 40.79528Everett, WA C4725 -122.229 47.98476Fairport Harbor, OH L3218 -81.2941 41.76666Fall River, MA C0189 -71.1588 41.72166Freeport, TX C2408 -95.3304 28.9384Galveston, TX C2417 -94.8127 29.31049Gary, IN L3736 -87.3251 41.61202Georgetown, SC C0772 -79.2896 33.36682Grays Harbor, WA C4702 -124.122 46.91167Gulfport, MS C2083 -89.0853 30.35216Hilo, HI C4400 -155.076 19.72861Honolulu, HI C4420 -157.872 21.31111Hopewell, VA C0738 -77.2763 37.32231


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Port Coordinates Port Name

US ACE Code Longitude Latitude

Houston, TX C2012 -95.2677 29.72538Indiana Harbor, IN L3738 -87.4455 41.67586Jacksonville, FL C2017 -81.6201 30.34804Kahului, Maui, HI C4410 -156.473 20.89861Kalama, WA C4626 -122.863 46.02048Lake Charles, LA C2254 -93.2221 30.22358Long Beach, CA C4110 -118.21 33.73957Longview, WA C4622 -122.914 46.14222Lorain, OH L3216 -82.1951 41.48248Los Angeles, CA C4120 -118.241 33.77728Manistee, MI L3720 -86.3443 44.25082Marblehead, OH L3212 -82.7091 41.52962Marcus Hook, PA C5251 -75.4042 39.81544Matagorda Ship Channel, TX C2410 -96.5641 28.5954Miami, FL C2164 -80.1832 25.78354Milwaukee, WI L3756 -87.8997 42.98824Mobile, AL C2005 -88.0411 30.72527Morehead City, NC C0764 -76.6947 34.71669Muskegon, MI L3725 -86.3501 43.19492Nawiliwili, Kauai, HI C4430 -159.353 21.96111New Bedford, MA C0187 -70.9162 41.63641New Castle, DE C0299 -75.5616 39.65668New Haven, CT C1507 -72.9047 41.29883New Orleans, LA C2251 -90.0853 29.91414New York, NY and NJ C0398 -74.0384 40.67395Newport News, VA C0736 -76.4582 36.98522Nikishka, AK C4831 -151.314 60.74793Oakland, CA C4345 -122.308 37.82152Olympia, WA C4718 -122.909 47.06827Other Puget Sound, WA C4754 -122.72 48.84099Palm Beach, FL C2162 -80.0527 26.76904Panama City, FL C2016 -84.1993 30.19009Pascagoula, MS C2004 -88.5588 30.34802Paulsboro, NJ C5252 -75.2266 39.82689Penn Manor, PA C0298 -74.7408 40.13598Pensacola, FL C2007 -87.2579 30.40785Philadelphia, PA C0552 -75.2022 39.91882Plaquemines, LA, Port of C2255 -89.6875 29.48Port Angeles, WA C4708 -123.453 48.1305Port Arthur, TX C2416 -93.9607 29.83142Port Canaveral, FL C2160 -80.6082 28.41409Port Dolomite, MI L3627 -84.3128 45.99139Port Everglades, FL C2163 -80.1178 26.09339Port Hueneme, CA C4150 -119.208 34.14824Port Inland, MI L3803 -85.8628 45.95508


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Port Coordinates Port Name

US ACE Code Longitude Latitude

Port Manatee, FL C2023 -82.5613 27.63376Portland, ME C0128 -70.2513 43.64951Portland, OR C4644 -122.665 45.47881Presque Isle, MI L3845 -87.3852 46.57737Providence, RI C0191 -71.3984 41.81178Redwood City, CA CCA05 -122.21 37.51306Richmond, CA C4350 -122.374 37.92424Richmond, VA C0737 -77.4194 37.45701Sacramento, CA CCA06 -121.544 38.56167San Diego, CA C4100 -117.178 32.70821San Francisco, CA C4335 -122.399 37.80667Sandusky, OH L3213 -82.7123 41.47022Savannah, GA C0776 -81.0954 32.08471Searsport, ME C0112 -68.925 44.45285Seattle, WA C4722 -122.359 47.58771South Louisiana, LA, Port of C2253 -90.6179 30.03345St. Clair, MI L3509 -82.4941 42.82663Stockton, CA C4270 -121.316 37.9527Stoneport, MI L3619 -83.4703 45.28073Tacoma, WA C4720 -122.452 47.28966Tampa, FL C2021 -82.5224 27.78534Texas City, TX C2404 -94.9181 29.36307Toledo, OH L3204 -83.5075 41.66294Two Harbors, MN L3926 -91.6626 47.00428Valdez, AK C4816 -146.346 61.12473Vancouver, WA C4636 -122.681 45.62244Wilmington, DE C0554 -75.507 39.71589Wilmington, NC C0766 -77.954 34.23928


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Table 3-103 Port RSZ Information Final RSZ End Point(s)

Port Name

RSZ Speed (knts)

RSZ distance (naut mi) Longitude Latitude

Albany, NY c 142.5 -73.8929 40.47993Alpena, MI e 3 -83.2037 44.99298Anacortes, WA a 108.3 -124.771 48.49074Anchorage, AK 14.5 143.6 -152.309 59.5608Ashtabula, OH e 3 -80.8097 42.08549Baltimore, MD c 157.1 -75.8067 36.8468Barbers Point, Oahu, HI 10 5.1 -158.132 21.21756

-89.4248 28.91161Baton Rouge, LA 10 219.8 -89.137 28.98883Beaumont, TX 7 53.5 -93.7552 29.55417Boston, MA 10 14.3 -70.7832 42.37881Bridgeport, CT 10 2 -73.1863 41.13906Brownsville, TX 8.8 18.7 -97.0921 26.06129

-80.9345 31.29955Brunswick, GA 13 38.8 -81.1357 30.68935Buffalo, NY e 3 -79.0996 42.81683Burns Waterway Harbor, IN e 3 -87.1032 41.80625Calcite, MI e 3 -83.5383 45.39496Camden-Gloucester, NJ c 94 -75.0095 38.79004Carquinez, CA 12 39 -122.632 37.76094Catalina, CA 12 11.9 -118.465 33.63641Charleston, SC 12 17.3 -79.6452 32.62557Chester, PA c 78.2 -75.0095 38.79004Chicago, IL e 3 -87.4141 41.86971Cleveland, OH e 3 -81.765 41.63079Conneaut, OH e 3 -80.5639 42.13361Coos Bay, OR 6.5 13 -124.359 43.35977Corpus Christi, TX d 30.1 -96.8753 27.74433Detroit, MI e 3 -83.1384 42.10308Duluth-Superior, MN and WI e 3 -91.8536 46.78916

-118.926 33.91252El Segundo, CA 12 23.3 -118.465 33.63641Erie, PA e 3 -80.115 42.3151Escanaba, MI e 3 -86.9224 45.58297Eureka, CA 12 9 -124.347 40.75925Everett, WA a 123.3 -124.771 48.49074Fairport Harbor, OH e 3 -81.3917 41.91401Fall River, MA 9 22.7 -71.3334 41.41708Freeport, TX c 2.6 -95.2949 28.93323Galveston, TX c 9.3 -94.6611 29.3247Gary, IN e 3 -87.2824 41.77658Georgetown, SC 12 17.6 -79.0779 33.1924


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Final RSZ End Point(s)

Port Name

RSZ Speed (knts)


Grays Harbor, WA a 4.9 -124.24 46.89509Gulfport, MS 10 17.4 -88.9263 30.11401Hilo, HI 10 7.1 -154.985 19.76978

-157.956 21.17658Honolulu, HI 10 10 -157.785 21.23827Hopewell, VA 10 91.8 -75.8067 36.8468Houston, TX c 49.6 -94.6611 29.3247Indiana Harbor, IN e 3 -87.4007 41.8401Jacksonville, FL 10 18.6 -81.3649 30.39769Kahului, Maui, HI 10 7.5 -156.44 21.01066Kalama, WA b 68.2 -124.137 46.22011Lake Charles, LA 6 38 -93.3389 29.73094

-118.465 33.63641Long Beach, CA 12 18.1 -118.13 33.45211Longview, WA b 67.3 -124.137 46.22011Lorain, OH e 3 -82.2701 41.64023

-118.465 33.63641Los Angeles, CA 12 20.6 -118.13 33.45211Manistee, MI e 3 -86.3819 44.41573Marblehead, OH e 3 -82.7293 41.69638Marcus Hook, PA c 94.7 -75.0095 38.79004Matagorda Ship Channel, TX 7.3 24 -96.2287 28.33472Miami, FL 12 3.8 -80.1201 25.75787Milwaukee, WI e 3 -87.6718 42.97343Mobile, AL 11 36.1 -88.0644 30.1457Morehead City, NC 10 2.2 -76.6679 34.68999Muskegon, MI e 3 -86.5377 43.29151Nawiliwili, Kauai, HI 10 7.3 -159.266 21.87705New Bedford, MA 9 22.4 -71.1013 41.38499New Castle, DE c 60.5 -75.0095 38.79004New Haven, CT 10 2.1 -72.9121 41.26588

-89.4248 28.91161New Orleans, LA 10 104.2 -89.137 28.98883New York, NY and NJ c 15.7 -73.8929 40.47993Newport News, VA 14 24.3 -75.8067 36.8468Nikishka, AK 14.5 90.7 -152.309 59.5608Oakland, CA 12 18.4 -122.632 37.76094Olympia, WA a 185.9 -124.771 48.49074Other Puget Sound, WA a 106 -124.771 48.49074Palm Beach, FL 3 3.1 -79.9973 26.77129Panama City, FL 10 10 -84.1797 30.0818Pascagoula, MS 10 17.5 -88.4804 30.09597Paulsboro, NJ c 83.5 -75.0095 38.79004Penn Manor, PA c 114.5 -75.0095 38.79004Pensacola, FL 12 12.7 -87.298 30.27777


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Final RSZ End Point(s)

Port Name

RSZ Speed (knts)


Philadelphia, PA c 88.1 -75.0095 38.79004-89.4248 28.91161

Plaquemines, LA, Port of 10 52.4 -89.137 28.98883Port Angeles, WA a 65 -124.771 48.49074Port Arthur, TX 7 21 -93.7552 29.55417Port Canaveral, FL 10 4.4 -80.5328 28.41439Port Dolomite, MI e 3 -84.2445 45.83181Port Everglades, FL 7.5 2.1 -80.082 26.08627Port Hueneme, CA 12 2.8 -119.238 34.10859Port Inland, MI e 3 -85.6524 45.87553Port Manatee, FL 9 27.4 -83.0364 27.59078Portland, ME 10 11.4 -70.1077 43.54224Portland, OR b 105.1 -124.137 46.22011Presque Isle, MI e 3 -87.082 46.5804Providence, RI 9 24.9 -71.3334 41.41708Redwood City, CA 12 36 -122.632 37.76094Richmond, CA 12 22.6 -122.632 37.76094Richmond, VA 10 106.4 -75.8067 36.8468Sacramento, CA 12 90.5 -122.632 37.76094San Diego, CA 12 11.7 -117.315 32.62184San Francisco, CA 12 14.4 -122.632 37.76094Sandusky, OH e 3 -82.5251 41.56193Savannah, GA 13 45.5 -78.0498 33.83598Searsport, ME 9 22.2 -68.7645 44.1179Seattle, WA a 133.3 -124.771 48.49074

-89.4248 28.91161South Louisiana, LA, Port of 10 142.8 -89.137 28.98883St. Clair, MI e 3 -82.5838 42.55923Stockton, CA 12 86.9 -122.632 37.76094Stoneport, MI e 3 -83.2355 45.25919Tacoma, WA a 150.5 -124.771 48.49074Tampa, FL 9 30 -83.0364 27.59078Texas City, TX c 15.1 -94.6611 29.3247Toledo, OH e 3 -83.3034 41.7323Two Harbors, MN e 3 -91.4414 46.93391Valdez, AK 10 27.2 -146.881 60.86513Vancouver, WA b 95.7 -124.137 46.22011Wilmington, DE c 65.3 -75.0095 38.79004Wilmington, NC 10 27.6 -80.325 31.84669

a Cruise speed through Strait of Juan de Fuca, then varies by ship type for remaining journey b Inbound on Columbia River at 6.5 knots, outbound at 12 knots c Speed varies by ship type similar to typical like port d Speed varies by ship DWTs e All Great Lake ports have reduced speed zone distances of 3 nautical miles with speeds halfway between service speed and maneuvering speed.


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APPENDIX 3B

Inventory Impacts of Alternative Program

The proposed program represents a comprehensive approach to reduce emissions from Category 3 marine diesel engines. As we developed this proposal, we evaluated an alternative, which considers the possibility of pulling ahead the CAA Tier 3 NOX standard from 2016 to 2014. NOX emissions were calculated for the year 2023 under three scenarios: Tier 1 only NOX standards (the base case), the coordinated strategy as proposed in NPRM which includes the 2016 NOX standards in effect 2016 (the primary case), and NOX standards for U.S.-vessels only pulled ahead to 2014 (the alternative case). This appendix describes the methodology that was used to estimate the NOX inventories for the proposed and alternative program scenarios in 2023.

The inventories described in this chapter are for calendar years 2002, 2020, and 2030. To calculate inventories for 2023, a spreadsheet model was developed and used. For both the proposed and alternative scenarios, it was assumed that the proposed ECA controls apply within 200 nautical miles for all 48 contiguous states. The only difference modeled was the different start dates for Tier 3. Note that only emissions from U.S. vessels are impacted by the proposed alternative.

Under the proposed base scenario, 48-state NOX emissions in 2023 are 10,494,636 short tons. With the coordinated strategy in effect (the primary case), 48-state NOX emissions in 2023 are 7,515,389 short tons, a 28.4 % reduction from Tier 1 only standards. Under the alternative scenario, 48-state NOX emissions in 2023 are 7,444,866 short tons, a difference of 0.9 percent from the primary case (Figure 3B-1).

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023Calendar Year

NO

X Em

issi

ons

(sho

rt to

ns p

er y

ear)

Tier 3 in 2016

Tier 3 in 2014 for U.S. ships

Figure 3B-1 NOX Emissions with the Primary Case (Tier 3 in 2016) versus the Alternative Case (Tier 3 in

2014 for U.S. vessels only)


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References

1 ICF International (March 2009). Inventory Contribution of U.S. Flagged Vessels, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-09-005, Docket ID EPA-HQ-OAR-2007-0121-0154. 2 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, Docket ID EPA-HQ-OAR-2007-0121-0063.1. 3 Corbett, J. et al. (April 2007). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel Inventories, Final Report, prepared by University of Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental Cooperation in North America, Contract Number 113.111, Docket ID EPA-HQ-OAR-2007-0121-0063.2. 4 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 5 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 6 RTI International (April 24, 2006). RTI Estimates of Growth in Bunker Fuel Consumption, Memorandum with spreadsheet from Michael Gallaher and Martin Ross, RTI, to Barry Garelick and Russ Smith, U.S. Environmental Protection Agency, Docket ID EPA-HQ-OAR-2007-0121-0063.4. 7 National Oceanic and Atmospheric Administration, Exclusive Economic Zone, Available online at http://nauticalcharts.noaa.gov/csdl/eez.htm. 8 U.S. Department of Interior, North American Atlas – Political Boundaries, Available online at http://www.nationalatlas.gov/mld/bound0m.html. 9 Browning, Louis (2009). ICF International, 2/16/2009 Email to Penny Carey, EPA, Docket ID EPA-HQ-OAR-2007-0121-0174 (and -0174.1). 10 US Department of Transportation Maritime Administration (May 2008). U.S. Water Transportation Statistical Snapshot, available from www.marad.dot.gov, Docket ID EPA-HQ-OAR-2007-0121-0170. 11 U.S. Army Corps of Engineers Navigation Data Center (2002). Principal Ports of the United States, available at http://www.iwr.usace.army.mil/ndc/db/pport/dbf/pport02.dbf, Docket ID EPA-HQ-OAR-2007-0121-0175 (and -0175.1). 12 Starcrest Consulting Group (June 2004). Port-Wide Baseline Air Emissions Inventory, prepared for the Port of Los Angeles, Docket ID EPA-HQ-OAR-2007-0121-0171.

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13 U.S. Army Corps of Engineers Navigation Data Center (2002), Vessel Entrances and Clearances available at http://www.iwr.usace.army.mil/ndc/db/entclrn/data/entrclrn02/, Docket ID EPA-HQ-OAR-2007-0121-0177 (and -0177.1 thru -0177.4). 14 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, Docket ID EPA-HQ-OAR-2007-0121-0063.1. 15 Nexus Media Communications (2005). The Motor Ship’s Guide to Marine Diesel Engines 2005, available at http://www.motorship.com/, Docket ID EPA-HQ-OAR-2007-0121-0163. 16 U.S. Army Corps of Engineers (2006). National Waterway Network, Available online at http://www.iwr.usace.army.mil/ndc/data/datanwn.htm, Downloaded April 2006. 17 ARCADIS Geraghty & Miller, Inc. (September 1999). Commercial Marine Activity for Deep Sea Ports in the United States, prepared for the U.S. Environmental Protection Agency, EPA Report Number: EPA420-R-99-020, available online at http://www.epa.gov/otaq/models/nonrdmdl/c-marine/r99020.pdf, Docket ID EPA-HQ-OAR-2007-0121-0150. 18 ARCADIS Geraghty & Miller, Inc. (September 1999). Commercial Marine Activity for Great Lake and Inland River Ports in the United States, prepared for the U.S. Environmental Protection Agency, EPA Report Number: EPA420-R-99-019, available online at http://www.epa.gov/otaq/models/nonrdmdl/c-marine/r99019.pdf, Docket ID EPA-HQ-OAR-2007-0121-0151. 19 ENVIRON International Corporation (2002). Commercial Marine Emission Inventory Development, prepared for the U.S. Environmental Protection Agency, EPA Report Number: EPA420-R-02-019, Docket ID EPA-HQ-OAR-2007-0121-0144. 20 California Air Resources Board (2005), 2005 Oceangoing Ship Survey, Summary of Results, Docket ID EPA-HQ-OAR-2007-0121-0149. 21 Entec UK Limited (2002). Quantification of Emissions from Ships Associated with Ship Movements between Ports in the European Community, prepared for the European Commission, Docket ID EPA-HQ-OAR-2007-0121-0059. 22 U.S. Environmental Protection Agency (January 2009). Category 3 Marine Engine CO and HC Emission Factors, Memorandum from Ari Kahan to Docket EPA-HQ-OAR-2007-0121, Docket ID EPA-HQ-OAR-2007-0121-0173. 23 U.S. Environmental Protection Agency (September 2007). Estimation of Particulate Matter Emission Factors for Diesel Engines on Ocean-Going Vessels, Memorandum from Mike Samulski to Docket EPA-HQ-OAR-2007-0121, Docket ID EPA-HQ-OAR-2007-0121-0060. 24 U.S. Environmental Protection Agency (September 2007). Estimation of Particulate Matter Emission Factors for Diesel Engines on Ocean-Going Vessels, Memorandum from Mike Samulski to Docket EPA-HQ-OAR-2007-0121, Docket ID EPA-HQ-OAR-2007-0121-0060. 25 Memo from Chris Lindhjem of ENVIRON (2005). PM Emission Factors, December 15, 2005, Docket ID EPA-HQ-OAR-2007-0121-0162. 26 U.S. Environmental Protection Agency, Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling – Compression Ignition (April 2004). Appendix C, EPA- 420-P-04-009,

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available online at http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf, Docket ID EPA-HQ-OAR-2003-0190-0411. 27 Energy and Environmental Analysis Inc. (February 2000). Analysis of Commercial Marine Vessels Emissions and Fuel Consumption Data, EPA420-R-00-002, available online at http://www.epa.gov/otaq/models/nonrdmdl/c-marine/r00002.pdf, Docket ID EPA-HQ-OAR-2007-0121-0146. 28 Starcrest Consulting Group (January 2007). Draft Port of Los Angeles Air Emissions Inventory for Calendar Year 2005, Docket ID EPA-HQ-OAR-2007-0121-0145. 29 Starcrest Consulting Group (April 2007). Puget Sound Maritime Air Forum Maritime Air Emissions Inventory, Docket ID EPA-HQ-OAR-2007-0121-0166. 30 Starcrest Consulting Group, LLC (April 2003). The New York, Northern New Jersey, Long Island Nonattainment Area Commercial Marine Vessel Emission Inventory, Vol 1 - Report, Prepared for the Port Authority of New York & New Jersey, United States and the Army Corps of Engineers, New York District, Docket ID EPA-HQ-OAR-2007-0121-0152. 31 Starcrest Consulting Group, LLC (November 2000). Houston-Galveston Area Vessel Emissions Inventory, Prepared for the Port of Houston Authority and the Texas Natural Resource Conservation Commission, Docket ID EPA-HQ-OAR-2007-0121-0147. 32 Eastern Research Group and Starcrest Consulting Group, LLC (January 2004). Update To The Commercial Marine Inventory For Texas To Review Emissions Factors, Consider A Ton-Mile EI Method, And Revise Emissions For The Beaumont-Port Arthur Non-Attainment Area Final Report, Submitted to the Houston Advanced Research Center, Docket ID EPA-HQ-OAR-2007-0121-0153. 33 Zuber M. Farooqui and Kuruvilla John (June 2004). Refinement of the Marine Emissions Inventory for the Corpus Christi Urban Airshed, Department of Environmental Engineering, Texas A&M University – Kingsville, Proceedings of the 97th Annual A&WMA Conf. & Exhibition, Docket ID EPA-HQ-OAR-2007-0121-0160. 34 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, October 2007, Docket ID EPA-HQ-OAR-2007-0121-0063.1. 35 ENVIRON International Corporation (March 2007). LADCO 2005 Commercial Marine Emissions, Docket ID EPA-HQ-OAR-2007-0121-0158. 36 California Air Resources Board (October 2005). Emissions Estimation Methodology for Ocean-Going Vessels, Docket ID EPA-HQ-OAR-2007-0121-0164. 37 E.H. Pechan & Associates Inc. (June 2005), Commercial Marine Inventories for Select Alaskan Ports, Final Report, Prepared for the Alaska Department of Conservation, Docket ID EPA-HQ-OAR-2007-0121-0165. 38 ICF International (March 2009). Inventory Contribution of U.S. Flagged Vessels, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-09-005, Docket ID EPA-HQ-OAR-2007-0121-0154.

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39 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 40 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial Marine Vessel Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final Report, prepared by University of Delaware for the California Air Resource Board, Contract Number 04-346, and the Commission for Environmental Cooperation in North America, Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 41 Lloyd’s Register and International Maritime Organization, Marine Exhaust Emission Quantification Study – Baltic Sea, in MEPC 45/INF.7. 1998. 42 ICF International (January 5, 2006). Current Methodologies and Best Practices in Preparing Port Emission Inventories, Final Report, prepared for the U.S. Environmental Protection Agency, available online at http://www.epa.gov/sectors/ports/bp_portemissionsfinal.pdf, Docket ID EPA-HQ-OAR-2007-0121-0148. 43 Corbett, J.J. and H.W. Koehler (2003). Updated Emissions from Ocean Shipping, Journal of Geophysical Research, 108(D20); p. 4650, Docket ID EPA-HQ-OAR-2007-0121-0176. 44 Corbett, J.J. and H.W. Koehler (2004). Considering Alternative Input Parameters in an Activity-Based Ship Fuel Consumption and Emissions Model: Reply to Comment by Oyvind Endresen et al. on “Updated Emissions from Ocean Shipping,” Journal of Geophysical Research. 109(D23303), Docket ID EPA-HQ-OAR-2007-0121-0159. 45 Levelton Consultants Ltd. (2006). Marine Emission Inventory Study Eastern Canada and Great Lakes – Interim Report 4: Gridding Results, prepared for Transportation Development Centre, Transport Canada, Docket ID EPA-HQ-OAR-2007-0121-0161. 46 IMO. Revision of MARPOL Annex VI and the NOx technical code. Input from the four subgroups and individual experts to the final report of the Informal Cross Government/Industry Scientific Group of Experts. BLG/INF.10 12/28/2007, Docket ID EPA-HQ-OAR-2007-0121-0118. 47 Transport Canada (2004). Transportation in Canada Annual Report 2004. (Tables 3-26 and 8-27). http://www.tc.gc.ca/pol/en/report/anre2004/8F_e.htm, Docket ID EPA-HQ-OAR-2007-0121-0169. 48 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 49 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3.

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50 Corbett, James and Chengfeng Wang (October 26, 2005). Emission Inventory Review SECA Inventory Progress Discussion, p 11, memorandum to California Air Resources Board, Docket ID EPA-HQ-OAR-2007-0121-0168. 51 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420-D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 53 California Air Resources Board ( September 2005). 2005 Oceangoing Ship Survey, Summary of Results, Docket ID EPA-HQ-OAR-2007-0121-0149. 54 U.S. Maritime Administration, Office of Statistical and Economic Analysis (April 2006). Vessel Calls at U.S. & World Ports, 2005, Docket ID EPA-HQ-OAR-2007-0121-0040. 55 Santa Barbara County Air Quality News, Issue 62, July-August 2001, Docket ID EPA-HQ-OAR-2007-0121-0167.

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